California Housing Market - Will it Crash?

							California Home Prices

Robert Stoll

Jason Nazar

What the Experts are Saying
“The main reason why “Historically rent prices house prices have been and salerapidly have in prices boom “The bigger the is the rising so moved together. If rents property prices level of historically low the go down the theory holds bigger the bust.” – The interest rates, which has that home prices will to Economist allowed households eventually follow.” - a CNN borrow more to buy Money – The Economist home.”
Introduction

The Data

Analysis

Predictions

Conclusions

Beginning Expectations
As Mortgage Rates

& As Population Growth & As Unemployment

& As Personal Income

Home Prices will Increase

Introduction

The Data

Analysis

Predictions

Conclusions

Objectives
Introduction

2. Identify Which 3. To Understand the 1.To Predict When Key Drivers ofa Combination that There Will Be Have Affected the Factors Would Cause Correction in the California a Residential and How Correction Market Residential Market Severe it Will Be

The Data

Analysis

Predictions

Conclusions

How Do Regressions Work
4 Important Indicators A Regression measures how in a Regression much the change in one •R Square variable (dependent) can be•P Value by the explained change in a separate •T Stat variable or set of variables (independent). •Coefficient

By themselves, regressions tell us NOTHING about economics!

Introduction

The Data

Analysis

Predictions

Conclusions

Data Source
Dependent Variable (what we are trying to

Introduction

explain): California Housing Prices from 1982 to 2004

Independent Variables (what indicators we
Population Labor Force

The Data

tested to explain the change in our dependent variable)
Rental Vacancies CPI Unemployment Rate Mortgage Loans Outstanding

Analysis

30 Year Fixed Mortgage Rate CA per capita personal income

Gross State Product Outstanding Consumer Credit

Predictions

Conclusions

Regressions
Dependent Variable: CA Home Prices from 1982-2004
R Square Adjusted R Square 0.988278809 0.976557618

Introduction

Coefficients Intercept -1667318.178

t Stat -2.401898067

P-value 0.035114425

The Data

CPI
Mortgage Loans Outstanding

-14492.86311
155.3372037

-2.799590159
3.381913856

0.017287438
0.006121806

California Per Capita Personal Income
30 Year Fixed Mortgage Rate Population Consumer Credit Outstanding Labor Force

23.87106866
13145.78201 0.103558054 0.000253176 0.01663074

2.000865703
1.946951954 2.330441439 0.00241383 0.756621464

0.07069901
0.077518914 0.039838063 0.998117264 0.465173652

Analysis

Predictions

Gross State Product
Unemployment Rate House Rental Vacancies

-0.68147785
2321.952932 -16896.6386

-1.764676976
0.371787642 -1.040540382

0.105328758
0.71711339 0.32041972

Conclusions

Apartment Rental Vacancies

3939.594102

0.322926706

0.752806047

Regressions
Dependent Variable:
CA Home Prices 1982-2004

Introduction

The Data
Independent Variable: Gross State Product 1982 – 2004

R Square
Adjusted R Square

0.957003309
0.954955847

Analysis

Coefficients
Intercept Gross State Product -2190926.44

t Stat
-15.1444518

P-value
8.9422E-14

Predictions

0.2057800

21.6196594

7.8601E-17

Conclusions

Regressions
Dependent Variable:
CA Home Prices 1982-2004

Introduction

Independent Variable: Rental Vacancies 1982 - 2004
R Square 0.89610636

The Data

Adjusted R Square

0.89115905 Coefficients t Stat P-value

Analysis

Intercept House Rental Vacancies

59284.2291

0.77371902 0.44768945

Predictions

185525.265

13.4584474 8.5408E-13

Conclusions

Regressions
Dependent Variable:
CA Home Prices 1982-2004

Introduction

The Data
Independent Variable: Consumer Credit 1982- 2004

R Square Adjusted R Square

0.8183169 0.8096654
Coefficients t Stat 4.7788340 9.7255297 P-value 0.0001012 3.15E-09

Analysis

Predictions

Intercept

69045.401 0.1232405

Consumer Credit Outstanding

Conclusions

Regressions
Dependent Variable:
CA Home Prices 1982-2004

Introduction

The Data
Independent Variable: Labor Force 1982 - 2004

R Square Adjusted R Square

0.796959611
0.787291021

Analysis

Coefficients Intercept
Labor Force 0.036364676

t Stat

P-value 9.1175E-06

Predictions

-353671.9471 -5.809485266

9.078970085 1.02297E-08

Conclusions

Regressions
Dependent Variable:
CA Home Prices 1982-2004

Introduction

The Data
Independent Variable: Mortgage Rates 1982 - 2004

R Square
Adjusted R Square

0.597707136
0.578550333 Coefficients t Stat P-value

Analysis

Intercept
30 Year Fixed Mortgage Rate

379461.3851
-19994.707

11.10589741
-5.58576555

2.9947E-10
1.5246E-05

Predictions

Conclusions

Regressions
Dependent Variable:
CA Home Prices 1982-2004

Introduction

The Data
Independent Variable: Unemployment Rate 1982 - 2004
R Square 0.22650025

Analysis

Adjusted R Square

0.18966693
Coefficients t Stat 5.37302988 P-value 2.4976E-06

Predictions

Intercept

1672401.78

Unemployment Rate

-108056.798 -2.47978432 .021704492

Conclusions

Regressions
Dependent Variable: CA Home Prices from 1982-2004
R Square
Adjusted R Square

Introduction

0.988278809
0.976557618

Coefficients
Intercept -1667318.178

t Stat
-2.401898067

P-value
0.035114425

The Data

CPI
Mortgage Loans Outstanding California Per Capita Personal Income 30 Year Fixed Mortgage Rate Population

-14492.86311
155.3372037 23.87106866 13145.78201 0.103558054

-2.799590159
3.381913856 2.000865703 1.946951954 2.330441439

0.017287438
0.006121806 0.07069901 0.077518914 0.039838063

Analysis

Consumer Credit Outstanding
Labor Force Gross State Product

0.000253176
0.01663074 -0.68147785

0.00241383
0.756621464 -1.764676976

0.998117264
0.465173652 0.105328758

Predictions

Unemployment Rate
House Rental Vacancies Apartment Rental Vacancies

2321.952932
-16896.6386 3939.594102

0.371787642
-1.040540382 0.322926706

0.71711339
0.32041972 0.752806047

Conclusions

Problems with the Data
Introduction

•Only had CA home prices since 1982 •Many factors were statistically significant

The Data

Analysis

Predictions

Conclusions

California Home Prices
The California housing market has traditionally been affected by a variety of factors that work together to increase the price of housing.
Labor Force
Population CPI Gross State Product Outstanding Consumer Credit Mortgage Loans Outstanding

Introduction

The Data

Analysis

Predictions

CA per capita personal income

Rental Vacancies

Conclusions

Per Mortgage Capita Home Change Loans Personal Year Prices in CPI Income 1982 1983 2.30% 1.64% 4.38% 5.21% 1984 -0.10% 4.95% 11.83% 9.91% 1985 4.90% 4.62% 13.80% 6.17% 1986 11.50% 3.13% 11.84% 4.25% 1987 6.30% 4.02% 13.74% 5.66% 1988 18.40% 4.64% 11.47% 5.84% 1989 16.60% 5.00% 11.25% 5.34% 1990 -1.20% 5.47% 12.45% 5.38% 1991 3.56% 4.15% 7.68% 0.46% 1992 -1.81% 3.56% 6.54% 3.03% 1993 -4.46% 2.61% 4.93% 0.81% 1994 -1.72% 1.41% 6.25% 2.26% 1995 -3.70% 1.65% 5.66% 4.24% 1996 -0.50% 2.01% 6.03% 4.25% 1997 5.20% 2.16% 6.75% 4.52% 1998 7.30% 1.99% 7.51% 6.48% 1999 8.70% 2.93% 9.91% 5.21% 2000 11.90% 3.74% 9.16% 8.92% 2001 -7.68% 3.95% 8.98% 0.90% 2002 28.86% 2.37% 10.86% 0.74% 2003 12.81% 2.69% 12.98% 1.00% 2004 20.71% 4.30% 9.17% 9.17%

30 Year Fixed Consumer Mortgage Credit Labor Rate Population Force -17.46% 4.83% -10.45% -18.02% 0.20% 1.27% -0.19% -1.84% -8.69% -9.30% -12.87% 14.64% -5.37% -1.51% -2.69% -8.68% 7.20% 8.20% -13.42% -6.17% -6.73% -5.08% 2.14% 1.89% 2.27% 2.46% 2.46% 2.44% 2.64% 2.35% 2.11% 1.74% 1.06% 0.67% 0.60% 0.79% 1.53% 1.26% 1.69% 1.36% 1.67% 1.64% 1.69% 1.77%

Gross State Product Unemply. -2.02% -19.59% -7.69% -6.94% -13.43% -8.62% -3.77% 13.73% 32.76% 20.78% 1.08% -8.51% -9.30% -7.69% -12.50% -6.35% -11.86% -5.77% 10.20% 24.07% 0.00% -8.96%

4.44% 0.85% 8.36% 12.80% 2.68% 13.72% 18.55% 2.94% 9.27% 15.60% 2.70% 7.18% 7.85% 3.04% 10.09% 5.97% 2.88% 9.68% 8.20% 2.72% 8.52% 6.50% 4.49% 7.45% 1.11% -0.29% 1.99% -0.94% 1.35% 2.07% 1.29% -0.68% 1.96% 7.75% 0.44% 3.68% 15.85% -0.38% 5.33% 13.81% 0.89% 5.13% 9.07% 2.70% 7.38% 4.48% 2.23% 7.66% 8.42% 1.47% 7.82% 7.59% 3.15% 9.62% 11.42% 1.66% 2.20% 7.37% 1.19% 5.28% 4.75% 0.48% 5.28% 5.27% 1.95% 5.28%

The Historical Key Drivers
Objective #1:
A variety of factors work together to promote the growth of the California residential market. For there to be a downturn in the California market there would have to be a large negative spike in one of 4 key factors (population, unemployment, per capita income, or mortgage rates) followed by a prolonged period of recession.
Introduction

The Data

To Understand the Key Drivers that Have Affected the California Residential Market

Analysis

Predictions

Conclusions

Will There be a Crash?

California home prices 2.63 have appreciated by 3.52 over %150 in the last three years! 5.37
From 1982 to 2004:
Personal Income Increased by a factor of:
Home Prices increased

Introduction

The Data

Analysis

by a factor of:

Predictions

Consumer Borrowing increased by a factor of:

Conclusions

Will There be a Crash?
700000 California Per Capita Personal Income Median Home Price Consumer Credit Outstanding Adjusted

Introduction

600000

500000

The Data

400000

Analysis
300000 200000

Predictions

100000

0

Conclusions
84 86 88 90 92 94 96 98 00 02 20 19 19 19 19 19 19 19 19 20 20 04

19

82

Yes Correction, How Bad?
There will be a correction Objective #2 because the market is overvalued right now.
Introduction

Identify which Combination of Factors would Cause the Correction and How Severe it Will be

However, this could happen in one of two ways. The factor most likely to change in the short run is mortgage rates. If mortgage rates increase, and all else remains equal, we will see home prices level off a bit, but not necessarily depreciate. But if increases in mortgage rates cause a recession, then we can expect housing prices to crash, and realign with the CPI.

The Data

Analysis

Predictions

Conclusions

If So, Then When?
450000 400000 350000 300000
Dollars

Median Home Price CPI With Multiplier

Look where 4 years & we are now house prices stopped rising

Introduction

The Data

250000 200000 150000 100000 50000 0
19 19 19 19 19 19 19 19 19 20 20 20

Analysis

Predictions

Conclusions

82

84

86

88

90

92

94

96

98

00

02

Year

04

Tell Me When!
Objective #3:in the Fed Watch for changes
Introduction

Fund Rate. As that slowly increases we will see mortgage Predict When rates rise at a much faster pace. As soon as this happens there will There Will be a a slowdown in the California Correction in the residential housing market. We predict that there will be the first Residential Market correction by January of 2005.

The Data

Analysis

Predictions

Conclusions

What You Should Do
1.Hold off on buying 2.Lock in a fixed rate 3.Be willing to hold for 5-10 years to wait out the correction 4.Remember the principle of: Buy Low, Sell High 5.Make your own decision
Introduction The Data

Analysis

Predictions

Conclusions