Very Good Presentation

California Home Prices

Robert Stoll

Jason Nazar

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

Introduction The Data

Analysis

Predictions

Conclusions

Beginning Expectations

Home Prices will Increase

As Mortgage Rates

Introduction The Data

& As Population Growth & As Unemployment & As Personal

Analysis

Predictions

Conclusions

Objectives Introduction

2. ToUnderstand Identify Which 3. Predict When 3.To the Key Drivers that Combination of Have Affected the Factors ThereWould Will BeCause a California a Residential Correction Correctionand in the Market How Severe it Market Will Be Residential

The Data

Analysis

Predictions

Conclusions

How Do Regressions Work

By themselves, regressions tell us NOTHING about economics!

4 Important Indicators AinRegression measures a Regression how much the change in •Rvariable Square (dependent) one can explained by the •Pbe Value change in a separate •T Stator set of variables variable (independent). •Coefficient

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 tested to explain the change in our dependent variable) Population

Labor Force

30 Year Fixed Mortgage Rate

Gross State Product

CA per capita personal income

Outstanding Consumer Credit

Rental Vacancies

The Data

Analysis

CPI Unemployment Rate Mortgage Loans Outstanding

Predictions

Conclusions

Regressions Dependent Variable: Home Prices from 1982-2004

CA

R Square

0.988278809

Adjusted R Square

0.976557618

Coefficients

Introduction

t Stat

P-value

Intercept

-1667318.178

-2.401898067

0.035114425

CPI

-14492.86311

-2.799590159

0.017287438

Mortgage Loans Outstanding

155.3372037

3.381913856

0.006121806

California Per Capita Personal Income

23.87106866

2.000865703

0.07069901

30 Year Fixed Mortgage Rate

13145.78201

1.946951954

0.077518914

Population

0.103558054

2.330441439

0.039838063

Consumer Credit Outstanding

0.000253176

0.00241383

0.998117264

0.01663074

0.756621464

0.465173652

Gross State Product

-0.68147785

-1.764676976

0.105328758

Unemployment Rate

2321.952932

0.371787642

0.71711339

House Rental Vacancies

-16896.6386

-1.040540382

0.32041972

Apartment Rental Vacancies

3939.594102

0.322926706

0.752806047

Labor Force

The Data

Analysis

Predictions

Conclusions

Regressions Introduction

Dependent Variable: CA Home Prices 1982-2004

The Data Independent Variable: Gross State Product 1982 – 2004 R Square

0.957003309

Adjusted R Square

0.954955847

Intercept Gross State Product

Analysis

Coefficients

t Stat

P-value

-2190926.44

-15.1444518

8.9422E-14

0.2057800

21.6196594

7.8601E-17

Predictions

Conclusions

Regressions Introduction

Dependent Variable: CA Home Prices 1982-2004

The Data

Independent Variable: Rental Vacancies 1982 - 2004 R Square

0.89610636

Adjusted R Square

0.89115905 Coefficients

Analysis t Stat

P-value

Intercept

59284.2291

0.77371902 0.44768945

House Rental Vacancies

185525.265

13.4584474 8.5408E-13

Predictions

Conclusions

Regressions Introduction

Dependent Variable: CA Home Prices 1982-2004

The Data Independent Variable: Consumer Credit 1982- 2004 R Square

0.8183169

Adjusted R Square

0.8096654

Analysis

Coefficients

t Stat

P-value

Intercept

69045.401

4.7788340

0.0001012

Consumer Credit Outstanding

0.1232405

9.7255297

3.15E-09

Predictions

Conclusions

Regressions Introduction

Dependent Variable: CA Home Prices 1982-2004

The Data Independent Variable: Labor Force 1982 - 2004 R Square

0.796959611

Adjusted R Square

0.787291021 Coefficients

Intercept Labor Force

Analysis

t Stat

-353671.9471 -5.809485266 0.036364676

P-value

Predictions

9.1175E-06

9.078970085 1.02297E-08

Conclusions

Regressions Introduction

Dependent Variable: CA Home Prices 1982-2004

The Data Independent Variable: Mortgage Rates 1982 - 2004 R Square

0.597707136

Adjusted R Square

0.578550333 Coefficients

Intercept 30 Year Fixed Mortgage Rate

Analysis t Stat

P-value

379461.3851

11.10589741 2.9947E-10

-19994.707

-5.58576555 1.5246E-05

Predictions

Conclusions

Regressions Introduction

Dependent Variable: CA Home Prices 1982-2004

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

0.22650025

Adjusted R Square

0.18966693

Intercept Unemployment Rate

Analysis

Coefficients

t Stat

P-value

1672401.78

5.37302988

2.4976E-06

-108056.798 -2.47978432

.021704492

Predictions

Conclusions

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

0.988278809

Adjusted R Square

0.976557618

Coefficients

Introduction

t Stat

P-value

Intercept

-1667318.178

-2.401898067

0.035114425

CPI

-14492.86311

-2.799590159

0.017287438

Mortgage Loans Outstanding

155.3372037

3.381913856

0.006121806

California Per Capita Personal Income

23.87106866

2.000865703

0.07069901

30 Year Fixed Mortgage Rate

13145.78201

1.946951954

0.077518914

Population

0.103558054

2.330441439

0.039838063

Consumer Credit Outstanding

0.000253176

0.00241383

0.998117264

0.01663074

0.756621464

0.465173652

Gross State Product

-0.68147785

-1.764676976

0.105328758

Unemployment Rate

2321.952932

0.371787642

0.71711339

House Rental Vacancies

-16896.6386

-1.040540382

0.32041972

Apartment Rental Vacancies

3939.594102

0.322926706

0.752806047

Labor Force

The Data

Analysis

Predictions

Conclusions

Problems with the Data Introduction

•Only had CA home prices since 1982

The Data

Analysis

•Many factors were statistically significant

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

The Data

Gross State Product

Population

Outstanding Consumer Credit

CPI

CA per capita personal income

Introduction

Mortgage Loans Outstanding Rental Vacancies

Analysis

Predictions

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.

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%

-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%

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.

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

Introduction The Data

Analysis

Predictions

Conclusions

Will There be a Crash?

California home 2.63 prices have appreciated by 3.52 over %150 in the last three 5.37 From 1982 to 2004: Personal Income Increased

Introduction The Data

by a factor of:

Analysis

Home Prices increased

by a factor of:

Consumer Borrowing increased by a factor of:

Predictions

Conclusions

Will There be a Crash? 700000

Introduction

California Per Capita Personal Income Median Home Price

600000

Consumer Credit Outstanding Adjusted

500000

The Data

400000

Analysis 300000

200000

Predictions

100000

Conclusions 20 04

20 02

0 20 0

8 19 9

6 19 9

4 19 9

2 19 9

19 90

19 88

6 19 8

4 19 8

19 82

0

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

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

Introduction The Data

Analysis

Predictions

Conclusions

If So, Then When? 450000 400000

Median Home Price

350000

CPI With Multiplier

Introduction

Look 4 years where & house we areprices now stopped rising

The Data

250000

Analysis

200000 150000 100000

Predictions

50000

Year

20 04

2 20 0

00 20

19 98

6 19 9

19 94

2 19 9

19 90

8 19 8

86 19

4 19 8

82

0 19

Dollars

300000

Conclusions

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

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 be a there will a slowdown in the Correction the California residentialin housing market. We predict that there Residential Market will be the first correction by January of 2005.

Introduction The Data

Analysis

Predictions

Conclusions

What You Should Do 1.Hold off on buying

Introduction

2.Lock in a fixed rate

The Data

3.Be willing to hold for 5-10 years to wait out the correction

Analysis

4.Remember the principle of: Buy Low, Sell High

Predictions

5.Make your own decision

Conclusions