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