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					Effects of the Economic Downturn on the Housing Market:

    Comparing Kenosha County to the United States



                      Matt Evans




                     Senior Thesis

             Geography and Earth Science

             Carthage College, Kenosha WI




                    April 1st 2010



               Adviser: Dr. Julio Rivera




                                                          1
Abstract:

This research is a study of the housing market of Kenosha County over the years of 2006 to

2009. The focus will be to compare trends in middle income home prices over these years in

comparison with the rest of the United States. The intent of this research is to prove that Kenosha

County housing prices were less affected by the economic downturn that started in 2006. Various

statistical and GIS analyses were conducted on census and MLS data sets to make a conclusion.




                                                                                                 2
                           Table of Contents


Introduction Pg. 4

Methods Pg. 8

Results Pg. 10
       Pg. 11 Table 1
       Pg. 12 Figure 1.1
       Pg. 12 Figure 1.2
       Pg. 13 Figure 1.3
       Pg. 13 Figure 1.4
       Pg. 13 Figure 2.1
       Pg. 14 Figure 2.2
       Pg. 15 Figure 3.1
       Pg. 15 Figure 3.2
       Pg. 16 Figure 3.3
       Pg. 16 Figure 4.1

Discussion Pg.17

Conclusion Pg.19

Acknowledgements Pg. 19

References Pg. 20

Appendix A Pg. 21

Appendix B Pg. 31




                                               3
Introduction

       The effects of the economic downturn in 2006 have many ties to the rise in home values

across the United States. These effects are felt the most by the Middle class families. This is a

problem that can be looked from many different angles and has many variables that change how

they affect different parts of the country. This research focuses on a single region, Kenosha

County in the south eastern Wisconsin area, to see if families in this area were being affected by

the recent economic downturn in a similar way as the country as a whole.

       The housing market as a whole is complex, with many different variables that affect the

selling and purchasing prices of homes. The most important part of home prices is the ability of

prospective buyers to acquire financing from a bank or mortgage lender. Housing market prices

are dependant on different metropolitan areas of the country at any given time. The research will

evaluate the differences in middle income housing across the country. There are obvious

differences among the cities of the U.S. in their average cost of living as well as their average

salaries. Variables that cause differences in housing costs include population growth and taxes.

My analysis of the overall housing market crisis will focus how different cities are affected.

       Another focus of this study is the banking aspect of qualifying for a home loan. There are

different ways in which credit availability allows people to get loans against their perspective

homes (Potepan, 1996). One way is the banking practices of allowing people to take out loans

based on the value of the home they are yet to buy. The value of the home acts as the collateral

against the loan, in case the homeowners are unable to make their payments. A second way of

understanding how banks decided the ability of people to get mortgage loans is based on their

income and other credit ratings. This method provides insight into what is middle class housing.

It also helps in the understanding of how the credit crisis in America is affecting middle income



                                                                                                     4
families and their ability to get mortgage loans. This research explores the effects of this

economic downturn on the housing market and how does it affect the middle class home buyers,

by understanding how middle income families can afford housing.

       The middle class was greatly affected because in many cases they were the ones that

were eligible for financing on homes, that were really out of there financial comfort zone. There

is a question on effectiveness of the government’s plans to help reduce the effects of the housing

crisis. Studies have described how the government attempted to help the people in this housing

crisis is not really helping the people it is supposed to. (Devit, 2008) Devit discusses how the

government intended on helping lower to middle income families in this housing crisis but the

money is not really getting to the people who need it the most. This is as an example of the

changes that are trying to be made by the government because of this crisis and how they are

affecting middle income homebuyers across the country. An article by Robert Manning, explains

how “the bubble has burst” on the American homebuyer and he blames it on the government and

the banks. (Manning, 2007) This article focuses on how this crisis will beset the middle income

families and put them in a credit crunch, where it will be very hard for middle to upper middle

class home owners to keep their homes if they have a subprime mortgage loan.

       During the housing boom of the late nineties, untill early 2006 when the market began to

fall, the prices of homes grew so rapidly, making it hard for middle income families to afford

housing putting a huge financial burden on those who did choose to become home owners. It is

speculated that the reason for the huge rise in foreclosures in the past few years is because of this

financial burden becoming to overwhelming for some families in this time of recession

(Rappaport, 2008). It is critical to determine the financial resources available to a family to know

what class of housing you could afford, based on income divided by the other costs of living with


                                                                                                    5
the remainder being your expendable income for housing. In my study, one research interest is

what types of homes middle income families are looking for in today’s market compared to the

homes they were purchasing in the middle of the housing boom a few years ago.

       In particular this study will try to determine if middle class households in Kenosha are

being greatly affected by the housing crisis. The effects of the credit crunch on families in the

middle class and the housing decisions they have to make concerning housing is critical in

understanding which areas will be most affected. Several variables affect a middle income family

in their housing decision. One study describes the differences in white middle class home

owners, focusing mainly on suburban home buyers and their different ties to the city (Wise,

1997). This study also gives other reasons why they middle class have flocked to the suburbs as

opposed to inter city living. There are benefits of homeownership compared to leasing. This may

be another reason that middle income families might be more likely to live in or around the

Kenosha area. Kenosha is more like a suburb within the city limits. It is made up more of homes

than apartment complexes.

       After academic journals were cited a few news paper articles were found on the topic. A

piece printed in the New York Times on July 29th 2009 predicts yet another slump in the housing

market based on the fact that unemployment has risen. Matt York states that nearly sixty percent

of the foreclosures in 2009 will be due to the rising nationwide unemployment rate. This is

different York claims from the first two waves that were brought on mainly by; “speculators who

walked away from properties whose value fell and subprime borrowers who were unable to pay

after an adjustable mortgage reset to a higher rate.”(York, 2009). An article in the Chicago

tribune by James P. Miller confirms the effect the crisis is having on the housing market in the

Chicago land area. In Millers March 2009 article he reports that according to the Illinois


                                                                                                    6
Association of Realtors that home sales in the Chicago land area went down by 40.4% from

March 2008. Also it is reported that home prices fell 27.8% state wide. The article also addresses

one of the reasons why, even with great buying conditions like low interest rates and extremely

low asking prices, people are not interested in buying homes right now. The reason Miller gives

is distrust in the economy as a whole, he says that many home buyers are waiting for the market

to stabilize before buying will begin to occur at the normal rate. In contrast to the two articles

above an article by John Krerowiczk of the Kenosha News reports that home sales in the

Kenosha area are up 15.5 percent in 2010. In the article a period of the growth over the last six

months in this area is explained. According to the article the reason for this growth is, “federal

tax credit for new home buyers and low interest rates” (Krerowiczk, 2010). This, Krerowiczk,

explains is not as good as it looks because sales fell consistently every year from 2007-2009. He

also warns that with the tax credits coming to an end near the end of April 2010, we could see a

slow down again in the amount of homes buying bought in the area.

       The overall basis of my research is to provide background source material to support my

theory that the Kenosha country area does not mirror the rest of the country when it comes to

middle income home values since 2000. Since most of my analysis is based on statistics that I

will be formulating, my source material is being used primarily for support and not so much a

basis for my conclusion.




                                                                                                     7
Methods

       Data from two different but related sources was used. The first source is the United States

Census Bureau. This data was accessed via the Census Bureau’s website, www.census.gov. On

this website, use of the American Fact Finder tab to locate specific data including:, the median

family income, for the United states. Then the process was repeated for the Wisconsin County of

Kenosha, and found the same variables for this area. Also data was taken from the National

Association of realtors pertaining to Core Based Statistical Area or CBSA in the United States

this data contained sold prices of homes sold in these areas from 2007 to 2009. MSA data or

Metropolitan Statistical Areas was taken from the Carthage College database and Median income

in these areas was used. The second data source that was used was the MLS data base provided

by Carthage College of Kenosha Wisconsin. From this data the median price of homes sold in

the Kenosha was found.

       The next step taken was defining the middle class, first by looking at the median income

of people living in the United States. This involved retrieving census data from the website. Then

the data from all fifty states was interpolated and the median income for the United States was

found. Then using the internet the next step was to research the average cost of living for the

United States. For this, the same census data was used but the country was broken down into the

four census regions: Northeast, South, Mid-West, and the West. The median income of these

areas was used to gauge the cost of living in each region. Then the cost of living for Kenosha

County was the next data needed. Again median income was used as a measure for the cost of

living. The next step is to define the affordability of homes to “middle class” home buyers. This

accomplished by finding the average percent a family or person can afford to spend on a home.

Then to account for taxes and furnishings and other expenses five percent was taken off the



                                                                                                    8
percent the average person would spend. From this a mortgage calculator was used in order to

calculate the average month payment a person could afford based on their income.

       The next step was performing statistical analysis of data from the Census Bureau and

MLS database at Carthage College. A least squared regression was used to see if there was a

significant correlation between the sold price of housing and the median income in the

Metropolitan Statistical areas as well as the Kenosha county area. Because the MSA and CBSA

data were not perfectly matched, manually matching the locations from each data set was

necessary to make the data from the MSA database match up with the CBSA data. Once this was

done all of the areas with data blanks were excluded from the analysis. This was done so outliers

in the data set could be kept to a minimum. One hundred and thirty metropolitan areas were

tested. Then the Kenosha county area was analyzed in census block groups as are outlined by the

U.S. Census Bureau. The point data from the MLS database was then joined to the polygons of

the blocks.

       After the statistical analysis the data put into Arc Map and analyzed by the GIS software.

An inverse distance weighted analysis was used with a one half mile buffer around each point to

weigh it. This is done to understand how much the cost of each home effects the cost of the

homes around it. This test was used on the MLS point data for Kenosha County. The Jenks

(natural breaks) classification was used as well. This classification uses a mathematical equation

that reduces the sum of squares differences within the groups. This analysis was used only on the

MLS points that were in the rage of middle class home buyers, $100,000- $210,000, outlined

earlier in the methods sections.




                                                                                                 9
Results



          After the data from the census was processed it was found that the median income for

the different regions of the country were as follows: the Northeast region reported a median

income of $45,481 annually, the Mid-West reported $42,414, the South was $38,790, and the

West reported $45,084 annually. These figures come from the 2000 nationwide census. The

median income of Kenosha County in southeastern Wisconsin, according to the 2000 census, is

$53,261. Note, that Kenosha county has a median income over ten thousand dollars higher than

the Mid-West’s median income. The national association of realtors’ provided table 1 of

Appendix A which provides the median sale price of existing single family homes, in

metropolitan statistical areas over the years of 2007- 2009 as well as a column that represents the

percent change of the sale price over the two years. This percent change column shows which

metropolitan statistical areas that were negatively affected over the past three years. Table 1 of

Appendix A is also supplemented by an additional column added, which contains data matched

from the MSA data set that includes the median income for these metropolitan areas. All cells

that contain the value -99.0 are representative of areas with no data. In Figure 0.1 the median

income for each state is shown. This table also shows twenty five percent of each state median

income which was decided to be the most the average family could afford per year for a

mortgage. The third column in the table shows, the monthly mortgage payment the average

person in each state could afford. This is interesting to note because it shows that across most

states the affordable monthly payment is fairly similar. However, housing prices across different

states are not nearly as close it price.




                                                                                                     10
Table 1
States                                   Median Incomes                      25% of Income                 Monthly Payment
New Hampshire                                                   $67,508                       $16,877.10                     $1,406.42
Maine                                                           $48,568                       $12,141.88                     $1,011.82
Rhode Island                                                    $55,639                       $13,909.81                     $1,159.15
Connecticut                                                     $65,976                       $16,493.97                     $1,374.50
Pennsylvania                                                    $51,156                       $12,788.99                     $1,065.75
New York                                                        $50,927                       $12,731.75                     $1,060.98
Massachusetts                                                   $60,038                       $15,009.61                     $1,250.80
Vermont                                                         $51,809                       $12,952.21                     $1,079.35
New Jersey                                                      $66,939                       $16,734.83                     $1,394.57
Maryland                                                        $66,618                       $16,654.39                     $1,387.87
Florida                                                         $47,062                       $11,765.60                      $980.47
Texas                                                           $46,853                       $11,713.28                      $976.11
Delaware                                                        $54,462                       $13,615.51                     $1,134.63
Oklahoma                                                        $44,154                       $11,038.49                      $919.87
Virginia                                                        $61,472                       $15,367.96                     $1,280.66
North Carolina                                                  $43,538                       $10,884.45                      $907.04
South Carolina                                                  $43,458                       $10,864.43                      $905.37
Alabama                                                         $42,946                       $10,736.52                      $894.71
Tennessee                                                       $41,978                       $10,494.60                      $874.55
Arkansas                                                        $40,507                       $10,126.75                      $843.90
West Virginia                                                   $40,910                       $10,227.44                      $852.29
Kentucky                                                        $41,427                       $10,356.77                      $863.06
Louisiana                                                       $40,476                       $10,119.02                      $843.25
Mississippi                                                     $37,416                        $9,354.01                      $779.50
Georgia                                                         $49,810                       $12,452.55                     $1,037.71
District of Columbia                                            $53,364                       $13,341.00                     $1,111.75
Minnesota                                                       $58,414                       $14,603.38                     $1,216.95
Illinois                                                        $53,251                       $13,312.70                     $1,109.39
Wisconsin                                                       $53,216                       $13,304.02                     $1,108.67
Nebraska                                                        $51,068                       $12,767.05                     $1,063.92
South Dakota                                                    $49,437                       $12,359.35                     $1,029.95
Indiana                                                         $48,095                       $12,023.80                     $1,001.98
North Dakota                                                    $47,494                       $11,873.60                      $989.47
Missouri                                                        $47,139                       $11,784.65                      $982.05
Ohio                                                            $48,978                       $12,244.61                     $1,020.38
Kansas                                                          $48,961                       $12,240.13                     $1,020.01
Michigan                                                        $51,001                       $12,750.35                     $1,062.53
Iowa                                                            $50,774                       $12,693.51                     $1,057.79
Alaska                                                          $63,217                       $15,804.18                     $1,317.02
Arizona                                                         $48,589                       $12,147.28                     $1,012.27
California                                                      $57,988                       $14,496.97                     $1,208.08
Colorado                                                        $61,304                       $15,325.93                     $1,277.16
Hawaii                                                          $64,193                       $16,048.15                     $1,337.35
Idaho                                                           $49,281                       $12,320.32                     $1,026.69
Nevada                                                          $55,570                       $13,892.51                     $1,157.71
Montana                                                         $44,043                       $11,010.64                      $917.55
New Mexico                                                      $43,636                       $10,908.96                      $909.08
Oregon                                                          $51,394                       $12,848.41                     $1,070.70
Utah                                                            $58,820                       $14,704.92                     $1,225.41
Washington                                                      $58,460                       $14,615.03                     $1,217.92
Wyoming                                                         $51,396                       $12,849.02                     $1,070.75

  Table 1 represents the median income of all 50 states as well as Washington DC. It also shows
  25% of the median income of each state which is the average most Americans spend on
  housing. Finally this table expresses the amount in which each states citizen can afford for a
  monthly mortgage payment.




                                                                                                                                   11
After this data was processed, similar data from Kenosha County was tabulated. The county was

broken down into census block groups and matched with the MLS housing data to achieve

average cost of homes in each block. Also the block group data for income is represented. These

figures are represented in Appendix A table 2. The results of Table 1 and 2 of appendix A were

then analyzed statistically using a least squared regression analysis. This was a regression of

Income to explain home values. Incomes represented X and home values represented Y. The

results of this analysis for the Metropolitan areas are expressed in figure 1.1 and 1.3 below. The

results of the Census block group data is expressed in figure 1.2 and 1.4. These results are also

expressed graphically in figures 2.1 for the MSA data and 2.2 for the Kenosha data. It is

important to notice the differences in R-squared for the MSA data compared to the Kenosha

County block group data. The residuals results of both these tests are shown in appendix B

tables 1 &2.
 Figure 1.1
   Regression Statistics for MSA Data
 Multiple R                 0.648760175
 R Square                   0.420889765
 Adjusted R Square          0.416365466
 Standard Error             71639.31499
 Observations                       130
 Figure 1.1 represents the statics of the least squared regression analysis of the MSA in the US



  Figure 1.2
     Regression Statistics for Kenosha
                  Blocks
 Multiple R                                 0.383633
 R Square                                   0.147174
 Adjusted R Square                          0.139693
 Standard Error                               16192.2
 Observations                                       116
 Figure 1.2 represents the statistics of the least squared regression analysis of the Kenosha
 County block groups




                                                                                                    12
 Figure 1.3
                                 Coefficients           Standard Error                 t Stat                   P-value
Intercept                       -259996.2552                44839.00157              -5.79844                        5.00954E-08
                      34829      10.13468184                1.040090578              9.744038                        4.30499E-17
 Figure 1.3 represents the results of the least squared regression analysis of the Metropolitan
 Statistical Areas in the US



Figure 1.4
                       Coefficients     Standard Error           t Stat                       P-value
Intercep                                                        22.9692
t                      125654.6717        5470.566651                   2                               1.3941E-44
                                                                4.43545
    81152              0.503324773        0.113477613                   4                             2.12882E-05
Figure 1.4 represents the results of the least squared regression analysis of Kenosha County




  Figure 2.1
                                       MSA Least Squared Regression
                  700000


                  600000


                  500000
    Home Values




                  400000


                  300000


                  200000


                  100000


                       0
                           0          10000          20000            30000            40000            50000        60000   70000
                                                                        Median Income

  Figure 2.1: This is a scatter plot of the Metropolitan Statistical Areas least squared regression
  analysis. It is an LSR of the data shown in Appendix A table 1of Income v.s. sold prices.




                                                                                                                                   13
  Figure 2.2
                                                Kenosha County Block Group Data
                   250000                          Least Squared Regression


                   200000



                   150000
     Home Prices




                   100000



                    50000



                        0
                            0   10000        20000        30000        40000        50000        60000   70000   80000   90000
                                                                       Median Income
   Figure 2.2: This is a scatter plot of the Kenosha County least squared regression analysis. It is
   the representation of the LSR of Appendix A table 2.




The next set of tests that was run was the Geographic Information mapping. Inverse distance

weighted analysis for the years of 2006 through 2008 was performed and are represented in

figures 3.1-3.3. It is interesting that throughout the three years the area of green (the cheapest

housing does not get larger, which is what one might expect from an area of already low home

prices. For the United States a least squared regression map was created of the metropolitan

statistical area comparing the median sold prices to the median income of the area. The results of

that analysis are shown in figure 4.1 below. Notice how the areas around the Mid-Western

Region are within two standard deviations of the mean.




                                                                                                                                 14
Figure3.1




Figure 3.1 is a map of Kenosha County; it indicates where homes of specific values are located
throughout the county. This allows us to locate where the most homes in our study area are
located in the year 2006.




   Figure3.2




    Figure 3.2 is a map of Kenosha County; it indicates where homes of specific values are located
    throughout the county. This allows us to locate where the most homes in our study area are
    located in the year 2007.

                                                                                                     15
Figure3.3




 Figure 3.3 is a map of Kenosha County; it indicates where homes of specific values are located
 throughout the county. This allows us to locate where the most homes in our study area are
 located in the year 2008.



 Figure 4.1




 Figure 4.1 is a Map of the United States. This figure shows Major metropolitan areas and the
 results of a least squared regression analysis in these areas. The regression was an analysis of
 Median incomes and median sold prices.

                                                                                                    16
Discussion:

       Following the results of this study, many conclusions can be made. The first and most

obvious conclusion is that Kenosha is full of middle class housing. In Appendix B table 2 you

can see that the median sale price in most census block groups is around $150,000. This fact in

conjunction with the fact that the median income of Kenosha county is $53,216 which would

allow those people near the median to afford homes up to $195,000 theoretically. This is based

on the table in which 25% of income is designated for housing. It is shown in the Figures 3.1-3.3

that the majority of these homes are located on the east side of the county. This is the area of the

county is the city of Kenosha. These are all homes that are middle income Americans could

afford. The highest regional income was the North-East with a regional median income of

$45,481 close to $10,000 lower than the Kenosha median. This being the highest shows that the

rest of the country as a whole does not have the financial resources of Kenosha County, which

makes sense as cost of living is different across the county. However, as stated earlier the median

income in conjunction with the low home values makes Kenosha an ideal area to defy the

housing market crunch, which the rest of the country was experiencing.

         After Performing the Least Squared Regression on the Kenosha Block Group data and

the MSA data many results were formulated. Both analysis’ posted a P-Value less than .0001

however the P value for the Kenosha County area was much lower, 1.394E-44, than the result of

the MSA analysis, 5.009E-08. This shows that both areas have a relationship between the home

sold values and the median income in these areas. The Kenosha Area also reported an R-Squared

of 0.14717 while The MSA data was 0.4277 this shows us that 14% of the Block group data can

be explained statistically by the regression while 42% of the MSA data is explained. This tells us

that in the United States a large portion of home sales can be explained by median incomes, forty



                                                                                                  17
two percent. It also tells us that only about fifteen percent of home values in Kenosha County can

be explained by the median incomes of the residents. These results are also expressed graphically

in figures 2.1-2.2 above. This is important because it shows that people are still buying houses at

a regular rate in the Kenosha area, thus the housing market has not been as affected as the rest of

the country, by the recent decline in income.

       During the course of this study certain problems arose that made an accurate analysis

difficult. One of these problems was the fact that the only available data that is accurate is the

data collected from the 2000 census. This meant that some of the median income data is almost

ten years old, and is being compared to the most recent housing data which is only two to four

years old. I think if this study were to be replicated it would be helpful to wait until the 2010

census data is posted and use the more recent numbers.

       Another problem that the research encountered was not having the MSA and CBSA data

match up perfectly. When the census switched formats they re-arranged some of the metropolitan

areas making them useless. For example in one data set the Kenosha and Racine metropolitan

areas are included in the Chicago metro areas, and in the other they are both their own separate

metro areas. This was not a huge obstacle to overcome but in the future having one

comprehensive data set would make the analysis both easier and more accurate.




                                                                                                     18
Conclusion:

       After the results of the testing were tabulated it can be concluded that in fact the Kenosha

county area has been less affected by the country’s economic crisis.

The country as a whole has been greatly affected by the economic crisis the changing rates of

sub-prime mortgage loans and following many foreclosures the effect of high unemployment

rates have lead to the county being in the middle not the end of a major housing crisis. It is clear

based on the statistics and visual representations of Kenosha County that this area has been

fairing much better than the rest of the county.



Acknowledgements:

A special thanks to all the members of the fall 2009 GEOS 400 class. I would also like to thank

my Advisor Julio Riviera for his guidance and support as well as the additional help from

Professor Joy Mast and Professor Thomas Groleau. Finally, I would like the thank Carthage

College




                                                                                                  19
References Cited



Devit, Caitlin "U.S. Housing Act Has Had Little Impact So Far, Panelists Say”. (Midwest Public
Finance Conference)(Discussion)." The Bond Buyer 365.33005 (Nov 11, 2008): 17.
Hornstein, Andreas. "Problems for a fundamental theory of house prices." Economic Quarterly
95.1 (2009)



Manning, Robert. "Subprime Mortgage Crisis Targeted by Rochester Institute of Technology
Consumer Finance Expert Robert Manning; 'Double Financial Bubble' Looming Large for
Middle-Income Communities, Explains Author." A Scribe Law News Service (August 14,
2007): NA.



Miller, James P. "Chicago Home Sales Sink 40%, Prices Fall 27%." Chicago Tribune [Chicago]
23 Mar. 2009, Business sec. Www.Chicagotribune.com. Web. 15 Mar. 2010.
<http://archives.chicagotribune.com/2009/mar/23/business/chi-biz-chicago-home-sales-prices-
march23>.



Potepan, Michael J. "Explaining intermetropolitan variation in housing prices, rents and land
prices." Real Estate Economics 24.n2 (Summer 1996): 219(27).



Rappaport,    Jordan. "The   affordability   of     homeownership        to    middle-income
Americans." Economic Review (Kansas City) 93.4 (Fall 2008): 65(31).



Wiese, Andrew. "Suburbia: middle class to the last?" Journal of Urban History 23.n6 (Sept
1997): 750(10).

York, Matt. "Times Topics: Housing." New York Times [New York City] 29 July 2009.
Www.nytimes.com. Web. 13 Mar. 2010.
<http://topics.nytimes.com/top/reference/timestopics/subjects/h/housing/index.html>.



                                                                                              20
Appendix A
Table 1
                                                 2008. 2009. 2009. 2009. 2009.         %     Med
Metropolitan Area         2007     2008   2009     4     1     2     3     4          Ch     Inc
                         144100.   1419   1406   1320 1416 1410 1406 1395                   37780
Champaign-Urbana, IL        0      00.0   00.0   00.0 00.0 00.0 00.0 00.00           5.70    .00
                         119300.   1005   9320   8610 5010 8800 1072 1057            22.8
Akron, OH                   0      00.0    0.0    0.0   0.0   0.0  00.0 00.0           0    -99.00
Albany-Schenectady-      198900.   1979   1891   1931 1845 1894 1954 1827                   34829
Troy, NY                    0      00.0   00.0   00.0 00.0 00.0 00.0 00.0            -5.4     .00
                         198500.   1926   1806   1837 1826 1822 1835 1756                   39088
Albuquerque, NM             0      00.0   00.0   00.0 00.0 00.0 00.0 00.0            -4.4     .00
Allentown-Bethlehem-     260800.   2436   2234   2380 2180 2256 2318 2151                   43098
Easton, PA-NJ               0      00.0   00.0   00.0 00.0 00.0 00.0 00.0            -9.6     .00
                         118400.   1247   1238   1226 1220 1273 1225 1222                   35679
Amarillo, TX                0      00.0   00.0   00.0 00.0 00.0 00.0 00.0            -0.3     .00
Anaheim-Santa Ana,       709500.   5332   4772   4648 4358 4681 4988 4950
CA (Orange Co.)             0      00.0   00.0   00.0 00.0 00.0 00.0 00.0             6.5   -99.00
                                                                                       -
                         130000.   1274   1156   1276   1103   1139   1235           99.0   47438
Appleton, WI                0      00.0   00.0   00.0   00.0   00.0   00.0   -99.0     0     .00
Atlanta-Sandy Springs-   172000.   1495   1234   1292   1156   1214   1294   1248           51948
Marietta, GA                0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -3.4    .00
                         269700.   2533   2213   2291   2191   2187   2230   2227
Atlantic City, NJ           0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -2.8   -99.00
                         183700.   1886   1874   1848   1823   1940   1891   1840           48950
Austin-Round Rock, TX       0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -0.4     .00
Baltimore-Towson,        286100.   2741   2512   2601   2458   2530   2611   2439           57291
MD                          0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -6.2     .00
                         384700.   3419   3212   3255   2767   3256   3197   3377           46034
Barnstable Town, MA         0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   3.7      .00
                         174400.   1650   1630   1564   1594   1685   1669   1574           38438
Baton Rouge, LA             0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   0.6      .00
Beaumont-Port            123000.   1274   1324   1326   1291   1386   1336   1261           35669
Arthur, TX                  0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -4.9     .00
                         111200.   1137   1157   1058   1103   1177   1142   1179    11.4   36374
Binghamton, NY              0      00.0   00.0   00.0   00.0   00.0   00.0    00.0    0       .00
Birmingham-Hoover,       161300.   1539   1461   1354   1304   1523   1533   1443           39278
AL                          0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   6.60     .00
                         152900.   1552   1566   1645   1533   1578   1572   1570           40148
Bismarck, ND                0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -4.6     .00
Bloomington-Normal,      154000.   1598   1528   1593   1538   1530   1572   1467           47021
IL                          0      00.0   00.0   00.0   00.0   00.0   00.0    00.0   -7.9     .00

                                                                                             21
                         206000.   1887    1538    1688    1571    1604    1534    1443     -     42570
Boise City-Nampa, ID        0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    14.5    .00
Boston-Cambridge-        395600.   3611    3329    3360    2907    3361    3480    3328           52792
Quincy, MA-NH**             0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -1.0    .00
                         376200.   3596    3460    3247    3284    3733    3583    3351
Boulder, CO                 0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    3.20   -99.00
Bridgeport-Stamford-     486600.   4379    3865    3806    3474    3802    3982    3737
Norwalk, CT                 0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -1.8   -99.00
Buffalo-Niagara Falls,   104000.   1054    1136    1062    9920    1154    1197    1107           38488
NY                          0      00.0    00.0    00.0     0.0    00.0    00.0    00.0    4.20     .00
                         110300.   9250    8620    8040    6620    1015    8930    8760           39457
Canton-Massillon, OH        0       0.0     0.0     0.0     0.0    00.0     0.0     0.0    9.00     .00
Cape Coral-Fort          252100.   1526    8760    1109    8730    8400    9800    9010      -
Myers, FL                   0      00.0     0.0    00.0     0.0     0.0     0.0     0.0    18.8   -99.00
                         136200.   1365    1397    1369    1273    1417    1457    1394           46206
Cedar Rapids, IA            0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    1.80     .00
                         122500.   1269    1268    1247    1192    1312    1320    1232           35418
Charleston, WV              0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -1.2     .00
Charleston-North         215400.   2062    1927    1938    1882    1982    1951    1870           39491
Charleston, SC              0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -3.5     .00
Charlotte-Gastonia-      204300.   1978    1891    1863    1715    1997    1996    1855           46119
Concord, NC-SC              0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -0.4     .00
                         130900.   1291    1226    1238    1179    1257    1241    1207           37411
Chattanooga, TN-GA          0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -2.5     .00
Chicago-Naperville-      276600.   2456    1992    2178    1856    2043    2101    1915      -    51046
Joliet, IL                  0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    12.1     .00
Cincinnati-              140800.   1318    1258    1160    1065    1296    1317    1250           44914
Middletown, OH-KY-IN        0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    7.80     .00
Cleveland-Elyria-        130000.   1085    1068    8830    6990    1060    1158    1101    24.7   42215
Mentor, OH                  0      00.0    00.0     0.0     0.0    00.0    00.0    00.0      0      .00
                         217500.   2055    1898    1870    1800    1890    1951    1898           46844
Colorado Springs, CO        0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    1.50     .00
                         147100.   1463    1467    1381    1526    1443    1488    1439           37485
Columbia, MO                0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    4.20     .00
                         146600.   1450    1459    1392    1343    1379    1440              -    41677
Columbia, SC                0      00.0    00.0    00.0    00.0    00.0    00.0    -99.0   99.0     .00
                         147400.   1393    1349    1265    1183    1366    1426    1325           44782
Columbus, OH                0      00.0    00.0    00.0    00.0    00.0    00.0     00.0   4.70     .00
                         136500.   1391    1343    1340    1263    1334    1378    1368           35773
Corpus Christi, TX          0      00.0    00.0    00.0    00.0    00.0    00.0     00.0   2.10     .00
                         109400.   9950    1183    9690    1149    1235    1221    1099    13.4   30916
Cumberland, MD-WV           0       0.0    00.0     0.0    00.0    00.0    00.0     00.0     0      .00
Dallas-Fort Worth-       150900.   1458    1451    1380    1357    1507    1505    1421           47418
Arlington, TX               0      00.0    00.0    00.0    00.0    00.0    00.0     00.0   3.00     .00
                                                                                                  31201
Danville, IL              -99.0    -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   -99.     .00
Davenport-Moline-        108700.   9420    1102    9810    1003    1132    1156    1067    8.80   40621

                                                                                                   22
Rock Island, IA-IL       0       0.0    00.0     0.0    00.0    00.0    00.0    00.0            .00
                      115600.   1070    1041    8780    7970    1065    1116    1064    21.2   41550
Dayton, OH               0      00.0    00.0     0.0     0.0    00.0    00.0    00.0     0      .00
                                8740    8670    7930    7710    9130    8850    8450           37859
Decatur, IL           83100.0    0.0     0.0     0.0     0.0     0.0     0.0     0.0    6.60    .00
Deltona-Daytona
Beach-Ormond Beach,   192300.   1641    1246    1436    1287    1272    1267    1226     -     35722
FL                       0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    14.6    .00
                      245400.   2193    2199    2008    1929    2237    2291    2232    11.2   51088
Denver-Aurora, CO        0      00.0    00.0    00.0    00.0    00.0    00.0    00.0     0      .00
                      149200.   1532    1493    1497    1373    1501    1566    1434           46651
Des Moines, IA           0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -4.2    .00
Detroit-Warren-       140300.                                                             -    49160
Livonia, MI              0      -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   99.0    .00
                      207500.   2062    1971    2124    2010    1937    2000    1953           40950
Dover, DE                0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -8.1    .00
                      178400.   1806    1769    1656    1715    1855    1843    1664
Durham, NC               0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   0.50   -99.00
                      131900.   1375    1326    1407    1328    1318    1328    1330           31051
El Paso, TX              0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -5.5     .00
                                8770    8730    8090    7710    8500    9450    8680           36415
Elmira, NY            81600.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   7.30     .00
                                9950    9790    9520    8590    9810    1028    9770           36627
Erie, PA              98100.0     0.0     0.0     0.0     0.0     0.0    00.0     0.0   2.60     .00
Eugene-Springfield,   239600.   2247    2037    2128    2108    2024    2066    1981           36942
OR                       0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -6.9     .00
                      140900.   1391    1402    1401    1341    1412    1421    1398           38069
Fargo, ND-MN             0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -0.2     .00
                      191100.   1906    1888    1849    1912    1886    1865    1843
Farmington, NM           0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -0.3   -99.00
                      124200.   1173    1145    1191    9850    1155    1213    1188           35144
Florence, SC             0       00.0    00.0    00.0     0.0    00.0    00.0    00.0   -0.3     .00
                                9260    9400    8860    8060    9460    1025    9350           42817
Ft. Wayne, IN         97100.0     0.0     0.0     0.0     0.0     0.0    00.0     0.0   5.50     .00
                      211100.   1886    1676    1740    1603    1782    1718    1658           31426
Gainesville, FL          0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -4.7     .00
                      134200.   1277    1192    1151    9200    1151    1266    1245           51046
Gary-Hammond, IN         0       00.0    00.0    00.0     0.0    00.0    00.0    00.0   8.20     .00
                      167600.   1611    1535    1476    1566    1524    1524    1545
Glens Falls, NY          0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   4.70   -99.00
                      129400.   1009    8730    8050    7200    8650    9710    8990    11.7   46116
Grand Rapids, MI         0       00.0     0.0     0.0     0.0     0.0     0.0     0.0     0      .00
                      150700.   1462    1377    1466    1384    1413    1353    1316      -    46447
Green Bay, WI            0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   10.2     .00
Greensboro-High       152000.   1453    1328    1354    1297    1418    1317    1279           40913
Point, NC                0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -5.5     .00
Greenville, SC        153600.   1557    1414    1469    1420    1400    1459    1378    -6.2   38458

                                                                                                23
                           0      00.0    00.0    00.0    00.0    00.0    00.0    00.0            .00
                        154500.   1402    1339    1295    1328    1387    1380    1282           38458
Gulfport-Biloxi, MS        0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -1.0    .00
Hagerstown-             208500.   1858    1571    1714    1671    1649    1519    1521      -
Martinsburg, MD-WV         0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    11.3   -99.00
Hartford-West
Hartford-East           263200.   2462    2320    2337    2223    2341    2375    2266           52188
Hartford, CT               0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -3.0    .00
                        643500.   6240    5962    6100    5700    5695    6022    6123           51914
Honolulu, HI               0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    0.40    .00
Houston-Baytown-        152500.   1516    1531    1421    1385    1574    1606    1500           44761
Sugar Land, TX             0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    5.60    .00
                        120500.   1112    1142    1002    9460    1213    1202    1115    11.3   45548
Indianapolis, IN           0      00.0    00.0    00.0     0.0    00.0    00.0    00.0      0     .00
                        139000.   1287    1349    1266    1226    1401    1412    1319           38887
Jackson, MS                0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    4.20    .00
                        189200.   1746    1459    1607    1541    1527    1457    1413      -    42439
Jacksonville, FL           0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    12.1    .00
Kalamazoo-Portage,                                                                               40710
MI                       -99.0    -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   -99.    .00
                        134500.   1308    1292    1256    1166    1322    1336    1279
Kankakee-Bradley, IL       0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   1.80   -99.00
                        153300.   1443    1407    1310    1266    1441    1462    1395           46193
Kansas City, MO-KS         0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   6.50     .00
Kennewick-Richland-     169200.   1661    1671    1659    1593    1639    1722    1681           44886
Pasco, WA                  0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   1.30     .00
                        258400.   2421    2084    2240    1943    2070    2066    2154
Kingston, NY               0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -3.8   -99.00
                        156400.   1491    1414    1417    1386    1447    1420    1393           36874
Knoxville, TN              0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -1.7     .00
                        126800.   9770    8070    8000    6560    8120    8660    8390           44441
Lansing-E.Lansing, MI      0        0.0     0.0     0.0     0.0     0.0     0.0     0.0   4.90     .00
Las Vegas-Paradise,     297700.   2205    1429    1817    1553    1418    1385    1394      -    42468
NV                         0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   23.3     .00
                        147500.   1443    1414    1382    1338    1427    1450    1407           39357
Lexington-Fayette,KY       0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   1.80     .00
                        137500.   1352    1333    1331    1324    1331    1360    1305           41850
Lincoln, NE                0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -2.0     .00
Little Rock-N. Little   129100.   1298    1318    1252    1254    1346    1325    1324           39145
Rock, AR                   0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   5.80     .00
Los Angeles-Long        593600.   4021    3339    3543    3035    3111    3456    3527           45903
Beach-Santa Ana, CA        0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -0.5     .00
                        137400.   1322    1311    1240    1211    1327    1356    1301           40821
Louisville, KY-IN          0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   4.90     .00
                                  2266    2123    2270    2085    2142    2179    2043      -    49223
Madison, WI             226500      00      00      00      00      00      00      00    10.0     .00
Manchester-Nashua,       -99.0    2428    2271    2386    2157    2226    2376    2252    -5.6   -99.00

                                                                                                  24
NH                                 00.0    00.0    00.0    00.0    00.0    00.0    00.0
                         137200.   1193    1192    1002    9610    1211    1293    1208    20.6   40201
Memphis, TN-MS-AR           0      00.0    00.0    00.0     0.0    00.0    00.0    00.0     0      .00
Miami-Fort
Lauderdale-Miami         365500.   2851    2112    2342    2060    2074    2170    1993     -     38632
Beach, FL                   0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    14.9    .00
Milwaukee-
Waukesha-West Allis,     223400.   2123    1934    1949    1908    2000    1995    1884           46132
WI                          0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -3.3    .00
Minneapolis-St. Paul-    225200.   2020    1812    1740    1741    1845    1720    1675           54304
Bloomington, MN-WI          0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -3.7    .00
                         136400.   1342    1276    1250    1279    1288    1283    1255           35629
Mobile, AL                  0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    0.40    .00
                         143800.   1352    1302    1263    1225    1342    1342    1262           37619
Montgomery, AL              0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -0.1    .00
Nashville-Davidson--                                                                         -    44223
Murfreesboro, TN          -99.0    -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   -99.0   99.0    .00
New Haven-Milford,       286500.   2638    2357    2404    2165    2362    2413    2371
CT                          0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   -1.4   -99.00
New Orleans-Metairie-    160300.   1605    1601    1549    1508    1658    1643    1579           35317
Kenner, LA                  0       00.0    00.0    00.0    00.0    00.0    00.0    00.0   1.90     .00
New York-Northern
New Jersey-Long          469700.   4379    3823    3914    3729    3798    3931    3759           50795
Island, NY-NJ-PA            0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -4.0    .00
New York-Wayne-          540300.   4943    4376    4592    4232    4252    4507    4340           50795
White Plains, NY-NJ         0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -5.5    .00
Norwich-New London,      267700.   2366    2118    2231    1996    2162    2171    2086           49283
CT                          0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -6.5    .00
                         380300.   3652    3322    3440    3209    3317    3438    3279           50795
NY: Edison, NJ              0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -4.7    .00
                         477200.   4358    3841    3821    3767    3868    3849    3832           50795
NY: Nassau-Suffolk, NY      0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    0.30    .00
NY: Newark-Union, NJ-    443700.   4172    3673    3744    3504    3794    3854    3415           50795
PA                          0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -8.8    .00
                         164600.   1375    1074    1217    1086    1102    1027    9320      -    31944
Ocala, FL                   0      00.0    00.0    00.0    00.0    00.0    00.0     0.0    23.4    .00
                         134900.   1281    1405    1242    1299    1283    1441    1363           36797
Oklahoma City, OK           0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    9.70    .00
                         138000.   1352    1337    1297    1290    1349    1376    1304           44981
Omaha, NE-IA                0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    0.50    .00
                         261300.   2089    1474    1752    1548    1492    1579    1396      -    41871
Orlando, FL                 0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    20.3    .00
Palm Bay-Melbourne-      183600.   1447    1070    1236    1143    1041    1095    1002      -    40099
Titusville, FL              0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    18.9    .00
Pensacola-Ferry Pass-    165600.   1557    1457    1517    1372    1478    1517    1425           36975
Brent, FL                   0      00.0    00.0    00.0    00.0    00.0    00.0    00.0    -6.1    .00
Peoria, IL               118600.   1221    1194    1172    1098    1261    1252    1119    -4.5   42986

                                                                                                   25
                            0       00.0   00.0   00.0   00.0    00.0   00.0    00.0            .00
Philadelphia-Camden-
Wilmington, PA-NJ-DE-     234900.   2314   2159   2125   2060    2110   2275    2126           47528
MD                           0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    0.00    .00
Phoenix-Mesa-             257400.   1913   1370   1559   1292    1311   1427    1439           44752
Scottsdale, AZ               0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -7.7    .00
                          120700.   1184   1189   1091   1034    1242   1246    1143           37467
Pittsburgh, PA               0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    4.80    .00
                          217400.   2126   1864   2060   1800    1890   2005    1731      -    38515
Pittsfield, MA               0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    16.0    .00
Portland-South
Portland-Biddeford,       242700.   2293   2035   2145   1921    2094   2028    2059           44707
ME                           0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -4.0    .00
Portland-Vancouver-       295200.   2801   2441   2645   2486    2462   2445    2394           46090
Beaverton, OR-WA             0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -9.5    .00
Providence-New
Bedford-Fall River, RI-   286500.   2506   2185   2245   2024    2157   2297    2204           41748
MA                           0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -1.8    .00
                          224200.   2234   2154   2309   2230    2113   2079    2195           48845
Raleigh-Cary, NC             0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -4.9    .00
                          154700.   1557   1528   1551   1452    1519   1564    1528           44714
Reading, PA                  0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -1.5    .00
                          321400.   2591   1938   2312   2098    1921   1922    1874      -    45815
Reno-Sparks, NV              0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    18.9    .00
                          233700.   2235   2112   1994           2112                     -    46800
Richmond, VA                 0      00.0   00.0   00.0   -99.0   00.0   -99.0   -99.0   99.0    .00
Riverside-San
Bernardino-Ontario,       379500.   2342   1697   2013   1725    1615   1681    1768     -
CA                           0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    12.2   -99.00
                          117900.   1170   1164   1125   1056    1191   1215    1121           43955
Rochester, NY                0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -0.4     .00
                          119300.   1170   1082   1115   1000    1134   1087    1083      -    44988
Rockford, IL                 0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    2.90     .00
Sacramento--Arden-        342800.   2167   1805   1879   1693    1775   1866    1886           46106
Arcade--Roseville, CA        0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    0.40     .00
Saginaw-Saginaw                     6220   5690   4390   3030    5570   6140    6740    53.5
Township North, MI        82100.0    0.0    0.0    0.0    0.0     0.0    0.0     0.0      0    -99.00
                          145400.   1332   1271   1137   1009    1336   1356    1268    11.5   44437
Saint Louis, MO-IL           0      00.0   00.0   00.0   00.0    00.0   00.0    00.0      0      .00
                          228300.   2088   1837   1980   2000    1912   1804    1767      -
Salem, OR                    0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    10.8   -99.00
                          232000.   2296   2170   2254   2301    2165   2189    2078           48594
Salt Lake City, UT           0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    -7.8     .00
                          153200.   1528   1493   1434   1452    1531   1528    1438           39140
San Antonio, TX              0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    0.30     .00
San Diego-Carlsbad-       588700.   3856   3595   3534   3305    3471   3781    3792           47067
San Marcos, CA               0      00.0   00.0   00.0   00.0    00.0   00.0    00.0    7.30     .00

                                                                                                26
San Francisco-          804800.   6220   4933   4871    4020   4729   5381   5513   13.2   62024
Oakland-Fremont, CA        0      00.0   00.0   00.0    00.0   00.0   00.0   00.0    0      .00
San Jose-Sunnyvale-     836800.   6680   5300   5250    4500   5000   5660   5850   11.4   62024
Santa Clara, CA            0      00.0   00.0   00.0    00.0   00.0   00.0   00.0    0      .00
Sarasota-Bradenton-     310900.   2406   1705   1781    1552   1758   1852   1713          40649
Venice, FL                 0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -3.8    .00
Seattle-Tacoma-         386900.   3572   3062   3259    3152   3284   3215   3055          50733
Bellevue, WA               0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -6.3    .00
Shreveport-Bossier      135600.   1385   1470   1392    1360   1468   1523   1493
City, LA                   0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   7.30   -99.00
                        144500.   1423   1392   1424    9550   1460   1372   1370          43387
Sioux Falls, SD            0      00.0   00.0   00.0     0.0   00.0   00.0   00.0   -3.8     .00
South Bend-                       8600   8520   8080    6180   8810   8850   8840          40420
Mishawaka, IN           90700.0    0.0    0.0    0.0     0.0    0.0    0.0    0.0   9.40     .00
                        128600.   1273   1207   1208    1091   1227   1272   1212
Spartanburg, SC            0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   0.30   -99.00
                        193800.   1912   1752   1859    1800   1778   1776   1701          37308
Spokane, WA                0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -8.5     .00
                        109000.   1080   1139   9670    1114   1162   1144   1118   15.6   43180
Springfield, IL            0      00.0   00.0    0.0    00.0   00.0   00.0   00.0    0       .00
                        211900.   2006   1864   1864    1701   1895   1954   1828          40740
Springfield, MA            0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -1.9     .00
                        122600.   1211   1146   1171    1163   1192   1138   1099          34661
Springfield, MO            0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -6.1     .00
                        121800.   1202   1210   1141    1137   1246   1252   1159          39750
Syracuse, NY               0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   1.60     .00
                        179500.   1799   1630   1501    1560   1498   1459   1518          36441
Tallahassee, FL            0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   1.10     .00
Tampa-St.Petersburg-    214900.   1730   1407   1515    1353   1409   1374   1402          37406
Clearwater, FL             0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -7.5     .00
                        106600.   9120   8340   7560    6550   8710   8830   8650          39902
Toledo, OH                 0       0.0    0.0    0.0     0.0    0.0    0.0    0.0   14.4     .00
                        111900.   1080   1084   1048    1065   1133   1111   1020          40988
Topeka, KS                 0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -2.7     .00
                        307100.   3032   2637   2478    2525   2543   2912   2469
Trenton-Ewing, NJ          0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -0.4   -99.00
                        244800.   2043   1725   1859    1764   1741   1740   1667     -    36758
Tucson, AZ                 0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   10.3     .00
                           -      1369   1301           1270   1332   1321   1281     -    38261
Tulsa, OK               99000.0   00.0   00.0   -99.0   00.0   00.0   00.0   00.0   99.0     .00
Virginia Beach-
Norfolk-Newport         226800.   2200   2100   2100    2010   2160   2150   2000          42448
News, VA-NC                0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   -4.8    .00
Washington-Arlington-
Alexandria, DC-VA-      430800.   3434   3087   2951    2794   3192   3247   3062          57291
MD-WV                      0      00.0   00.0   00.0    00.0   00.0   00.0   00.0   3.80    .00
Waterloo/Cedar Falls,   112800.   1115   1106   1052    9730   1067   1182   1137   8.10   37266

                                                                                            27
IA                      0      00.0    00.0    00.0      0.0   00.0   00.0   00.0            .00
                     115600.   1218    1187    1182     1081   1253   1204   1152           42651
Wichita, KS             0      00.0    00.0    00.0     00.0   00.0   00.0   00.0   -2.5     .00
                     274600.   2371    2177    2170     1896   2203   2241   2219
Worcester, MA           0      00.0    00.0    00.0     00.0   00.0   00.0   00.0   2.30    -99.00
                     156500.   1533    1552    1459     1435   1628   1584   1499           34828
Yakima, WA              0      00.0    00.0    00.0     00.0   00.0   00.0   00.0   2.70      .00
Youngstown-Warren-             7170    6650    6170     5120   7150   7070   7270   17.8    36255
Boardman, OH-PA      78900       0       0       0        0      0      0      0      0       .00




Appendix A
Table 2

                                                         Households: Median household income in
                                                                          1999
 FIPS           TRACT    BLKGRP       Avg. Sold Price
 550590001001   000100   1                155,000.00                                       81,152.00
 550590001002   000100   2                138,575.00                                       29,943.00
 550590001003   000100   3                151,466.67                                       29,314.00
 550590001004   000100   4                162,250.00                                       40,976.00
 550590001005   000100   5                132,000.00                                       42,500.00
 550590003003   000300   3                118,725.00                                       36,250.00
 550590003004   000300   4                141,000.00                                       35,975.00
 550590003005   000300   5                135,866.67                                       34,000.00
 550590004001   000400   1                137,722.22                                       47,417.00
 550590004002   000400   2                138,734.55                                       47,667.00
 550590004003   000400   3                127,983.33                                       39,688.00
 550590004004   000400   4                157,510.00                                       52,250.00
 550590004005   000400   5                139,991.67                                       56,979.00
 550590005001   000500   1                147,883.33                                       46,500.00
 550590005002   000500   2                158,042.00                                       45,203.00
 550590005003   000500   3                137,918.44                                       51,023.00
 550590005004   000500   4                172,500.00                                       17,794.00
 550590005005   000500   5                140,021.43                                       41,736.00
 550590006003   000600   3                159,406.80                                       63,389.00
 550590006004   000600   4                159,384.00                                       47,768.00
 550590006005   000600   5                130,000.00                                       39,969.00
 550590006006   000600   6                159,368.50                                       76,887.00
 550590006007   000600   7                173,500.00                                       47,869.00
 550590006008   000600   8                143,404.45                                       42,261.00
 550590007001   000700   1                121,575.00                                       36,429.00

                                                                                              28
550590007002   000700   2   187,401.11   65,954.00
550590007003   000700   3   150,000.00   35,417.00
550590007004   000700   4   142,985.71   22,386.00
550590007005   000700   5   120,666.67   35,972.00
550590008001   000800   1   142,500.00   23,403.00
550590008002   000800   2   120,375.00   36,042.00
550590008003   000800   3   126,750.00   41,196.00
550590009001   000900   1   120,574.00   31,250.00
550590009002   000900   2   124,125.00   35,625.00
550590009003   000900   3   125,645.75   42,857.00
550590009004   000900   4   133,350.00   30,345.00
550590009005   000900   5   153,000.00   31,250.00
550590010001   001000   1   185,800.00    9,861.00
550590010002   001000   2   137,900.00   14,253.00
550590010003   001000   3   158,360.00   21,000.00
550590011001   001100   1   152,450.00   24,516.00
550590011003   001100   3   107,500.00   23,802.00
550590012002   001200   2   127,037.50   50,000.00
550590012003   001200   3   141,503.64   37,500.00
550590012004   001200   4   169,068.57   38,664.00
550590013001   001300   1   137,950.00   48,704.00
550590013002   001300   2   148,200.00   42,212.00
550590013003   001300   3   156,580.00   31,932.00
550590013004   001300   4   163,768.75   41,556.00
550590014001   001400   1   159,631.67   55,365.00
550590014002   001400   2   162,970.00   61,389.00
550590014003   001400   3   177,800.00   59,299.00
550590014004   001400   4   152,866.67   59,306.00
550590014005   001400   5   145,381.82   53,125.00
550590014006   001400   6   154,741.64   46,790.00
550590015001   001500   1   111,350.00   45,288.00
550590015002   001500   2   133,785.71   42,768.00
550590015003   001500   3   144,242.71   39,891.00
550590015004   001500   4   141,833.80   53,304.00
550590015005   001500   5   148,290.00   39,464.00
550590016001   001600   1   134,000.00   27,731.00
550590016002   001600   2   145,475.00   34,042.00
550590016003   001600   3   133,501.25   35,875.00
550590017001   001700   1   126,500.00   31,786.00
550590017002   001700   2   125,000.00   57,708.00
550590017003   001700   3   143,119.23   54,100.00


                                            29
550590017004   001700   4   141,983.57   47,434.00
550590018001   001800   1   167,000.00   32,500.00
550590018002   001800   2   128,350.00   38,750.00
550590018003   001800   3   144,652.86   46,000.00
550590019001   001900   1   177,875.00   71,250.00
550590019002   001900   2   126,000.00   41,810.00
550590019003   001900   3   172,475.00   49,000.00
550590020001   002000   1   168,427.22   66,046.00
550590020002   002000   2   172,466.67   62,292.00
550590020003   002000   3   148,815.00   72,727.00
550590021001   002100   1   158,128.00   52,917.00
550590021002   002100   2   146,485.71   44,250.00
550590021003   002100   3   136,300.00   36,066.00
550590022001   002200   1   148,847.65   54,559.00
550590022002   002200   2   148,345.00   39,643.00
550590022003   002200   3   155,516.00   52,552.00
550590023001   002300   1   138,650.00   41,732.00
550590023002   002300   2   164,125.00   52,143.00
550590023003   002300   3   127,825.00   61,474.00
550590023004   002300   4   149,733.33   41,860.00
550590023005   002300   5   140,680.00   46,023.00
550590024001   002400   1   152,488.89   47,039.00
550590024002   002400   2   144,414.09   59,375.00
550590024003   002400   3   187,250.00   71,490.00
550590025001   002500   1   153,584.62   67,833.00
550590026002   002600   2   157,074.19   62,589.00
550590026003   002600   3   156,080.00   60,054.00
550590026004   002600   4   163,681.94   62,895.00
550590026005   002600   5   130,225.00   64,792.00
550590027001   002700   1   173,536.84   66,875.00
550590027002   002700   2   179,950.00   56,029.00
550590027003   002700   3   176,090.00   41,791.00
550590028003   002800   3   146,500.00   40,278.00
550590028004   002800   4   200,000.00   66,250.00
550590028005   002800   5   152,090.00   55,625.00
550590029011   002901   1   161,131.63   57,794.00
550590029012   002901   2   141,160.00   48,077.00
550590029013   002901   3   161,887.50   55,833.00
550590029014   002901   4   148,036.84   41,731.00
550590029015   002901   5   153,444.44   38,565.00
550590029021   002902   1   156,915.38   56,217.00


                                            30
550590029022    002902   2              146,310.71                         51,971.00
550590029023    002902   3              173,550.00                         68,125.00
550590029024    002902   4              159,711.11                         51,471.00
550590030001    003000   1              127,190.00                         38,095.00
550590030002    003000   2              169,083.33                         47,813.00
550590030003    003000   3              163,400.00                         53,654.00
550590030004    003000   4              165,381.88                         58,906.00
550590030005    003000   5              154,600.00                         46,625.00
550590030006    003000   6              153,013.64                         57,500.00
550590030007    003000   7              152,969.40                         67,813.00


Appendix B
Table 1
 RESIDUAL OUTPUT KENOSHA BLOCK GROUPS                              PROBABILITY OUTPUT

 Observation    Predicted Y    Residuals      Standard Residuals     Percentile      Y
            1   48426.88945    32725.11055          2.595175163      0.427350427    9861
            2   43531.90062   -13588.90062         -1.077630504      1.282051282   14253
            3   47373.88323   -18059.88323         -1.432189521      2.136752137   17794
            4   50587.53901   -9611.539013         -0.762216748      2.991452991   21000
            5   41572.41498    927.5850198          0.073559587      3.846153846   22386
            6   37616.19113   -1366.191128         -0.108342041      4.700854701   23403
            7   44254.60064   -8279.600643         -0.656591027      5.555555556   23802
            8   42724.76141   -8724.761413         -0.691893282       6.41025641   24516
            9   43277.75525    4139.244753          0.328251456      7.264957265   27731
           10   43579.44845    4087.551546          0.324152068       8.11965812   29314
           11   40375.36545   -687.3654529         -0.054509633      8.974358974   29943
           12   49174.92123    3075.078769           0.24386069      9.829059829   30345
           13   43954.09651    13024.90349          1.032904259      10.68376068   31250
           14   46305.97597    194.0240262          0.015386543      11.53846154   31250
           15   49333.46821   -4130.468206         -0.327555456      12.39316239   31786
           16   43336.23219    7686.767807          0.609578045      13.24786325   31932
           17   53642.25046   -35848.25046         -2.842847211       14.1025641   32500
           18   43962.96617    -2226.96617         -0.176603446      14.95726496   34000
           19   49740.20676    13648.79324          1.082380124      15.81196581   34042
           20   49733.41189    -1965.41189         -0.155861601      16.66666667   35417
           21   40976.37372   -1007.373722         -0.079887011      17.52136752   35625
           22   49728.79257    27158.20743          2.153707175      18.37606838   35875
           23   53940.27109   -6071.271092         -0.481465507      19.23076923   35972
           24    44971.1777   -2710.177699         -0.214923211      20.08547009   35975
           25   38465.54992   -2036.549921         -0.161503007      20.94017094   36042
           26   58083.08897    7870.911029          0.624181018      21.79487179   36066

                                                                              31
27   46936.78631   -11519.78631   -0.913545067    22.64957265   36250
28   44846.38446   -22460.38446   -1.781159206     23.5042735   36429
29   38194.84785   -2222.847849   -0.176276854    24.35897436   37500
30   44701.63159   -21298.63159   -1.689029579    25.21367521   38095
31   38107.92517   -2065.925166   -0.163832531    26.06837607   38565
32   40007.80668    1188.193323    0.094226414    26.92307692   38664
33   38167.23127   -6917.231271    -0.54855206    27.77777778   38750
34   39225.50253   -3600.502525   -0.285527981    28.63247863   39464
35    39678.7174    3178.282603    0.252044987    29.48717949   39643
36   41974.74283   -11629.74283   -0.922264868    30.34188034   39688
37   47830.84819   -16580.84819   -1.314898703     31.1965812   39891
38   57605.92483   -47744.92483   -3.786280352    32.05128205   39969
39   43330.73669   -29077.73669    -2.30593018    32.90598291   40278
40   49428.23877   -28428.23877   -2.254423528    33.76068376   40976
41   47666.93685   -23150.93685   -1.835921569    34.61538462   41196
42   34270.90957   -10468.90957   -0.830208168    35.47008547   41556
43   40093.48761    9906.512392    0.785608803    36.32478632   41731
44   44404.69467   -6904.694669   -0.547557879    37.17948718   41732
45   52619.61396   -13955.61396      -1.1067117   38.03418803   41736
46   43345.63772    5358.362276    0.424930228    38.88888889   41791
47   46400.34917   -4188.349173   -0.332145547    39.74358974   41810
48   48897.76205   -16965.76205   -1.345423241     40.5982906   41860
49   51040.15784   -9484.157844   -0.752115133    41.45299145   42212
50   49807.22167    5557.778334    0.440744372    42.30769231   42261
51   50802.11387    10586.88613    0.839563977    43.16239316   42500
52    55221.7598    4077.240203    0.323334355    44.01709402   42768
53   47791.11211    11514.88789    0.913156612    44.87179487   42857
54   45560.47285    7564.527146    0.599884084    45.72649573   44250
55   48349.89369   -1559.893693   -0.123703092    46.58119658   45203
56   35418.28899    9869.711013     0.78269037    47.43589744   45288
57   42104.59468    663.4053248     0.05260954    48.29059829   46000
58   45220.99639   -5329.996395    -0.42268075    49.14529915   46023
59   44503.09024    8800.909756    0.697932017             50   46500
60   46427.17103    -6963.17103   -0.552195186    50.85470085   46625
61   42168.45624   -14437.45624   -1.144922881    51.70940171   46790
62   45588.24296   -11546.24296   -0.915643139    52.56410256   47039
63   42019.81845    -6144.81845   -0.487297979    53.41880342   47417
64   39933.30152    -8147.30152   -0.646099408    54.27350427   47434
65   39486.27058    18221.72942     1.44502429    55.12820513   47667
66   44886.17513     9213.82487    0.730677118    55.98290598   47768
67   44547.72522    2886.274781    0.228888108    56.83760684   47813
68     52003.137     -19503.137   -1.546642804    57.69230769   47869
69   40484.63968   -1734.639684   -0.137560844    58.54700855   48077

                                                           32
 70   45343.22743    656.7725731    0.052083548    59.4017094   48704
 71   55244.11134    16005.88866    1.269303113   60.25641026   49000
 72   39784.29121    2025.708795    0.160643282   61.11111111   50000
 73   53634.79995   -4634.799947   -0.367550102   61.96581197   51023
 74   52428.47867    13617.52133    1.079900192   62.82051282   51471
 75   53632.31644    8659.683559    0.686732461   63.67521368   51971
 76   46583.63186    26143.36814    2.073228128   64.52991453   52143
 77   49359.09798     3557.90202    0.282149664   65.38461538   52250
 78   45889.45667   -1639.456666   -0.130012616   66.23931624   52552
 79   42853.90369   -6787.903686    -0.53829609   67.09401709   52917
 80   46593.36136    7965.638643    0.631693132   67.94871795   53125
 81   46443.56216   -6800.562164   -0.539299936    68.8034188   53304
 82    48580.6681    3971.331904    0.314935588   69.65811966   53654
 83   43554.25216   -1822.252164   -0.144508711   70.51282051   54100
 84   51146.32769    996.6723071    0.079038365   71.36752137   54559
 85   40328.17885    21145.82115    1.676911367   72.22222222   55365
 86   46857.31414   -4997.314138   -0.396298296   73.07692308   55625
 87   44159.23404    1863.765958    0.147800849   73.93162393   55833
 88   47678.52654   -639.5265383   -0.050715899   74.78632479   56029
 89   45272.07016    14102.92984    1.118394182   75.64102564   56217
 90   58038.05474    13451.94526    1.066769635    76.4957265   56979
 91   48005.07564    19827.92436    1.572399176   77.35042735   57500
 92   49045.04192    13543.95808     1.07406646   78.20512821   57708
 93   48748.75173    11305.24827    0.896531716   79.05982906   57794
 94   51014.28631    11880.71369    0.942167424   79.91452991   58906
 95   41043.42836    23748.57164    1.883315358   80.76923077   59299
 96    53951.2508     12923.7492    1.024882495   81.62393162   59306
 97   55862.50415    166.4958502    0.013203497   82.47863248   59375
 98   54712.14452   -12921.14452   -1.024675938   83.33333333   60054
 99    45893.7141   -5615.714103   -0.445338809   84.18803419   61389
100   61837.81777    4412.182235    0.349896014   85.04273504   61474
101   47559.64942    8065.350579    0.639600513    85.8974359   62292
102   50254.24124    7539.758765    0.597919896   86.75213675   62589
103   44302.28394    3774.716056    0.299343507   87.60683761   62895
104   50479.50654    5353.493465    0.424544121   88.46153846   63389
105   46351.72475   -4620.724755   -0.366433907   89.31623932   64792
106   47963.30181   -9398.301806   -0.745306556   90.17094017   65954
107   48997.71358     7219.28642    0.572505715   91.02564103   66046
108   45837.30306    6133.696944    0.486416018   91.88034188   66250
109   53955.17212    14169.82788    1.123699348   92.73504274   66875
110   49830.89775    1640.102251    0.130063812   93.58974359   67813
111   40138.93575   -2043.935754   -0.162088721   94.44444444   67833
112   52624.01331   -4811.013313   -0.381524219    95.2991453   68125

                                                           33
          113      50930.26274       2723.737263       0.215998515   96.15384615   71250
          114      51520.90237       7385.097629       0.585654918   97.00854701   71490
          115       48307.6812        -1682.6812      -0.133440419   97.86324786   72727
          116      47834.91211       9665.087889       0.766463294   98.71794872   76887
          117      47821.72876       19991.27124       1.585352952   99.57264957   81152




Appendix B
Table 2
 RESIDUAL OUTPUT For MSA

                                                   Standard
 Observation    Predicted House$$    Residuals     Residuals
           1          94813.68162   87886.31838     1.231571748
           2          137354.9302   38245.06983     0.535937201
           3          177409.0289   37690.97106     0.528172484
           4          103303.9519   18896.04807     0.264794787
                                               -
           5         265807.7257    141007.7257    -1.975974583
                                               -
           6         235862.0429    51862.04291    -0.726755063
                                               -
           7         319176.5661    75276.56606    -1.054868309
           8         206735.4215    130964.5785     1.835237588
           9         130862.3705    26537.62948     0.371878073
          10         103204.0664     22895.9336     0.320846128
          11         110245.9965    7654.003515      0.10725736
          12         139252.7553    5047.244704     0.070728233
          13         147942.7967    9057.203324     0.126920731
                                               -
          14         216594.1236    69894.12357    -0.979442871
                                               -
          15         172135.0728    27835.07279    -0.390059453
          16         274238.0647    58561.93528     0.820642238
                                               -
          17         131361.7982    20661.79818    -0.289538661
                                               -
          18         141040.7063    53440.70634    -0.748877247
                                               -
          19         208453.4526    69053.45263     -0.96766235
          20         100696.9395    22503.06048     0.315340704
          21         141380.3172    45619.68285     0.639279396
                                               -
          22         207584.4485    22084.44849    -0.309474596
          23         120604.1263    95.87373311     0.001343501
          24         256798.0506               -   -0.915037015

                                                                              34
                   65298.05064
                             -
25   195548.2417   70548.24175   -0.988609183
                             -
26   168589.1364   58489.13637    -0.81962209
                             -
27   214826.1496   25026.14964   -0.350697349
28   121343.2792   22556.72079    0.316092659
                             -
29   194229.7527   61729.75271   -0.865033612
30   104242.8759   32557.12405    0.456230673
31   55728.47252   54171.52748    0.759118416
                             -
32   220559.5792   78459.57924   -1.099472625
                             -
33   152667.3824   45967.38239   -0.644151792
                             -
34   161946.7484   55546.74842   -0.778389712
                             -
35   125078.9981   40578.99815   -0.56864309
36   103733.4597   18866.54027   0.264381287
                             -
37   257217.5699   34017.56988   -0.476696246
                             -
38   212898.3588   69498.35885   -0.973896926
39   155953.6164   39346.38357    0.551370171
                             -
40   110655.5272   23855.52717   -0.33429314
41   57076.92722   75923.07278   1.063927961
                             -
42   112773.1005   15073.10047   -0.211222919
43   115919.4948   82180.50524     1.15161484
44   127176.5943   12623.40566    0.176894767
45   97960.07591   20839.92409    0.292034781
                             -
46   174602.2455   81102.24546   -1.136504931
47   60822.63471   104977.3653    1.471072627
                             -
48   256798.0506   132298.0506   -1.853923848
                             -
49   207554.4828   117654.4828   -1.648720071
                             -
50    210860.694   79260.69397   -1.110698835
                             -
51     155584.04   27684.03996   -0.387942994
52   131062.1416   6737.858418    0.094419202
                             -
53   131062.1416   2862.141582   -0.040107866
                             -
54   268204.9785   41604.97852   -0.583020396
55   265468.1149   346831.8851    4.860237167
                             -
56   194019.9931   44019.99309   -0.616862566
57   201880.9845             -   -1.266530095


                                                35
                   90380.98455
                             -
58    135347.231   3447.230952   -0.048306862
                             -
59   170826.5723   29526.57231   -0.413762835
                             -
60   208323.6014   68823.60143   -0.964441391
                             -
61   195268.5623   27168.56226   -0.380719483
62   115240.2731   24059.72686    0.337154638
63    190823.656   -106923.656    -1.49834646
                             -
64   171116.2404   31716.24035   -0.444447171
65    140041.851   658.1489924    0.009222797
                             -
66   164943.3144   34443.31441   -0.482662304
                             -
67   137924.2777   5524.277706   -0.077413009
68    205426.921    147273.079    2.063772459
                             -
69   154665.0931   24565.09305   -0.344236454
                             -
70    238588.918   34288.91796    -0.48049871
71     148472.19     -27672.19   -0.387776938
72   132800.1499   66499.85014    0.931878115
                             -
73   207714.2997   19314.29968   -0.270655846
                             -
74   289340.7573   121840.7573   -1.707383325
75   102804.5243   22695.47573    0.318037065
76   122681.7454   3518.254645    0.049302134
77   99688.09564   58211.90436    0.815737172
78   254290.9238   73609.07624     1.03150138
79   254290.9238   87209.07624    1.222081394
80   254290.9238   121609.0762    1.704136723
81   254290.9238   128909.0762    1.806433348
82   254290.9238   179709.0762    2.518305753
                             -
83   239188.2312   30588.23116   -0.428640112
84   65996.70532   27203.29468    0.381206197
85   114471.1545   21828.84547     0.30589277
                             -
86   196217.4748   65817.47482   -0.922315828
                             -
87    165153.074   25553.07403   -0.358081265
                             -
88   147453.3576   47253.35756   -0.662172466
89    116249.117   26250.88298    0.367859827
                             -
90    176290.311   64390.31097   -0.902316644
                             -
91   221658.3201   9058.320101    -0.12693638
                             -
92   193930.0961   50030.09611    -0.70108356

                                                36
                              -
93    121163.4853   6863.485252   -0.096179641
94    131631.4891   41468.51088    0.581108043
95    193480.6112   12419.38879     0.17403583
96    207294.7804   32105.21956    0.449898029
97     163924.482   56475.51803     0.79140478
                              -
98    234813.2448   15313.24481   -0.214588118
                              -
99    193550.5311   40750.53109   -0.571046822
                              -
100   204547.9283   17147.92828   -0.240297971
                              -
101   185969.2191   73869.21913   -1.035146824
                              -
102   196287.3947   87987.39469   -1.232988155
                              -
103   207454.5973   18854.59729   -0.264213927
                              -
104   190783.7018   63983.70182   -0.896618734
                              -
105   232306.1179   24506.11793   -0.343410022
106   137874.3349   5925.665061    0.083037745
107    217053.597    162146.403    2.272195862
108   366452.3889   184847.6111    2.590313257
109   366452.3889   218547.6111     3.06255932
110   152947.0619   18352.93812    0.257184059
111   253671.6335   51828.36654    0.726283148
                              -
112   180295.7208   43295.72085   -0.606713168
                              -
113   150659.6832   62259.68318   -0.872459653
114   119575.3053   50524.69472    0.708014487
                              -
115   178228.0903   66428.09031   -0.930872527
116   153856.0202   28943.97977    0.405598828
117   93135.60467   16764.39533    0.234923433
                              -
118   143967.3525   28067.35246   -0.393314443
119   110915.2296   40884.77044     0.57292795
120   120554.1835    19645.8165    0.275301469
                              -
121   145485.6126   58985.61256   -0.826579328
                              -
122   156333.1815   54333.18146   -0.761383711
123    114081.601   52618.39904     0.73735406
                              -
124   129094.3966   994.3965801   -0.013934714
125   170916.4693   29083.53071    0.407554389
                              -
126   319176.5661   12976.56606   -0.181843687
                              -
127    119155.786   5455.786037   -0.076453219
128   172944.1456             -   -0.809182358

                                                 37
                    57744.14561
129   94803.69307   55096.30693   0.772077569
                              -
130   109057.3586   36357.35864   -0.509484258




                                                 38

				
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