Annals of the

University of North Carolina Wilmington

International Masters of Business Administration

http://csb.uncw.edu/imba/

BUSINESS SUCCESS AND REGIONAL REAL ESTATE VALUES

Eduard A. Al-Tememy

A Thesis Submitted to the

University of North Carolina Wilmington in Partial Fulfillment

of the Requirements for the Degree of

Master of Business Administration

Cameron School of Business

University of North Carolina Wilmington

2013

Approved by

Advisory Committee

Clay Moffett Robert Burrus

J. Edward Graham

Chair

Accepted by

Dean, Graduate School

ii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................................ iii

ABSTRACT ...................................................................................................................................... iv

ACKNOWLEDGEMENTS ................................................................................................................v

ABBREVIATIONS .......................................................................................................................... vi

CHAPTER 1: INTRODUCTION .......................................................................................................1

CHAPTER 2: REVIEW OF LITTERATURE ...................................................................................4

CHAPTER 3: DATA DESCRIPTION .............................................................................................10

CHAPTER 4: METHODOLOGY ....................................................................................................13

CHAPTER 5: DISCUSSION AND RESULTS ................................................................................18

CHAPTER 6: CONCLUSION .........................................................................................................23

TABLES ...........................................................................................................................................25

BIBLIOGRAPHY .............................................................................................................................33

APPENDIX .......................................................................................................................................37

iii

LIST OF TABLES

Table Page

1. Dependent variables with descriptions ..................................................................................... 24

2. Independent national variables with descriptions ..................................................................... 25

3. Independent regional variables with descriptions ..................................................................... 26

4. Regressions CS 20 city index ................................................................................................... 28

5. Regressions 15 cities ................................................................................................................. 29

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ABSTRACT

This Research studies the relationship between regional real estate values in Case Shiller

cities and corporate performance of large publicly traded companies in the respective areas.

Various national factors that influence real estate prices in the US are similarly tested. Study was

performed on data ranging from February 2000 – March 2013.

We find that most cities’ real estate values are loosely correlated with the performance of

the S&P 500 companies headquartered within the cities. While real estate prices around the US

continued to fall from early in the examined period (late 2006) until late in the examined period

(early 2012), in two cities we see the converse: Seattle real estate prices and Washington DC

values moved upward, with the stock market, in contrast with other cities in the US. That makes

intuitive sense: Seattle, with its strong tech presence, and Washington, with its growing

government presence since the election of 2008, are both witnessing continued growth in home

values. Other cities, such as New York and Phoenix, were no so well situated as the financial and

real estate crises unfolded.

v

ACKNOWLEDGEMENTS

First of all I would like to thank my family for the support they have given me throughout

the Graduate program, especially during the last few months while finishing my thesis. The

support and belief in me has meant a lot and helped me to stay motivated.

Furthermore I would like to address my gratitude towards my thesis committee. Special

thanks to Dr. Graham for inspiring me to pursue the research within real estate and the constant

support and guidance which was needed to realize this paper. Moreover I would like to thank Dr.

Burrus for his support, sharing his expertise in econometrics and providing valuable inputs and

guidance along the way. I would also like to thank Dr. Moffett for his unique teaching skills in

class and the valuable inputs and comments while writing my thesis.

I would also like to thank the faculty staff for the graduate program at the Cameron

Business School for being helpful, friendly and at the same time professional.

“Land monopoly is not only

monopoly, but it is by far the greatest

of monopolies; it is a perpetual

monopoly, and it is the mother of all

other forms of monopoly.”

-Winston Churchill

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ABBREVIATIONS

S&PCS20 Standard & Poor’s Case Shiller 20 City Index

PWI Price Weighted Index

GDP Gross Domestic product

INDPRO Industrial Production Index (IPI)

UNEMP Unemployment Rate

DISPPI Disposable Personal Income

15YMTGR 15 Year Mortgage Rates

CPI Consumer Price Index

CONSENT Consumer Confidence Sentiment Index

S&P500 Standard & Poor 500 Index

MSACSR Monthly Supply of Homes in the US Ratio

NHSUS New Homes Sold in US

USD US Currency International Abbreviation

FRED Federal Reserve Economic Data

NAFTA North American Free Trade Agreement

NBER National Bureau of Economic Research

CHAPTER 1: INTRODUCTION

Schulz & Werwatz (2004) emphasizes the importance of real estate and housing prices to

investors, developers, banks and policy makers. The American real estate market and the price

development regarding it are also of importance as it may give a picture of the American economy.

The American economy has been steadily growing since the 1990’s, and a part of the

growth may be explained by the NAFTA agreement, which came into force on January 1st 1994

1.

This trilateral trade bloc allowed American multi-national companies with capital resources to

benefit from protected investment and cheap labor. Gould (1998) explained that the NAFTA

agreement had been a success for the United States and Mexico as it had a significant positive

effect on trade flows between the two. However, Burfisher et al. (2001) stated that the agreement

had a relatively small positive effect on the U.S. economy whilst it had a relatively large positive

effect on the Mexican economy. Never the less previous studies proves that the agreement has had

an overall positive financial impact on the US economy.

While booming, the economy experienced some financial setbacks. In the last two decades,

the financial sector in the US experienced anomalies in form of financial recessions which made an

impact on the overall economy. Early 1990’s recession saw the GDP declining a respectable -

1.4% due to the combined effects from the debt accumulation in the 1980’s, increase in inflation

and interest rates, 1990 oil price shock and decreased consumer confidence. This demonstrated the

growing importance of financial markets to the American economy (Walsh, 1998; Knoop, 2010).

Around the millennium change another recession played its role. The dotcom bubble2, as

1 NAFTA, North American Free Trade Agreement; signed in 1992 and implemented in 1994. Allowed for

an orderly adjustment to free trade with Mexico and Canada as it eliminated tariffs and non-tariff barriers. 2 Rapid increase in equity markets due to heavy investments in internet-based companies.

2

speculative as it was, collapsed in 2001 and provided, together with the September 11 attacks3, a

small decline in the American economy after a long period of growth through the 1990’s. The

downfall in GDP was – 0.3% (Kliesen, 2003).

The third and by far the most influential setback was the great recession which lasted from

December 2007 through June 2009 (NBER, 2010). In this period the GDP dropped – 4.3%. The

subprime mortgage crisis led to the collapse of the housing bubble in the United States. This crisis

saw housing prices plunge and the falling housing related assets led to the worldwide financial

crisis. This global phenomenon affected the United States financial sector to the greatest extent.

(Dao & Loungani, 2010). As the crisis evolved major financial institutes in the US experienced a

failure and collapse thus forcing the government to respond with an unprecedented bailout and a

fiscal stimulus package4. Furthermore Wilkerson (2009) explained that different sectors of the

economy and different parts of the US entered and exited the financial crisis at different points in

time, thus the effect and the length of the crisis was different in different regions.

A real estate market may be a major mechanism which can affect changes in stock prices.

In the case of Hong Kong, it was stated that investors often await news on real estate sales before

making short-term trades within the stock marked (Tse 2001). This is further explained by

Bjørnland & Jacobsen (2012), their study indicated that by allowing the interest rate and asset

prices to react simultaneously to news in the market the two reacted differently in their roles to the

monetary transmission mechanism. By being subject to a contractionary monetary policy shock the

stock prices had an immediate negative reaction while the fall in housing prices were more

gradual. They also explain that the stock prices play a more important role on the short term

3 A series of four coordinated terrorist attacks launched on American soil by the terrorist group Al-Qaeda.

4 A 700 Billion bank bailout package and a 787 Billion fiscal stimulus package

3

interest rate than housing prices, thus the shocks that occur in housing prices has a larger impact on

GDP and inflation than stock prices.

Therefore it is of significance to study the development of corporate performance on the

stock market, in addition to the underlying factors in the economy that the US has faced, both

before and during the period of study.

Much of this has given me the motivation to examine whether or not the stock prices may

have an effect on the real estate prices. However this study looks to shed light upon whether or not

the stock prices are influencing the housing prices in the cities defined by the Case Shiller 20 City

Index. The null hypothesis is that there is a correlation between corporate performance and

regional real estate value. Furthermore, underperforming companies will be located in cities with

underperforming real estate markets.

Firstly the percentage change of SPCS20 index will be examined against the percentage

change of the S&P500 index. Then each city with a number of four large S&P500 companies will

have a Price weighted Index created and the percentage change of this index will serve as the key

variable in our model. Moreover some key macroeconomic factors will be added to the models.

We examine some earlier studies of this issue in the next section. We then describe our data

collection and provide some descriptive statistics. Employing the data we gathered, we build

a model to measure the importance of factors describing our dependent variable. In our closing

pages, we report our results and provide a summary. We consider the implications of our findings,

and suggest a couple of ideas for subsequent research.

CHAPTER 2: REVIEW OF LITTERATURE

Previous research state that real estate and stock markets tend to move in the same direction

due to the fact that real estate prices together with stock prices are affected by the same economic

activities. Such variables may have a similar impact on both markets, negatively and positively,

depending on the increase or decrease in the variables. Quan and Titman (1997) explains that even

though there can be a positive effect due to increases in the economic activities, some of the

increases may affect the two markets differently. Increased investment opportunities, and increased

corporate profits may boost the stock prices and put upward pressure on real interest rates which

might reduce the real estate values. Moreover higher levels of foreign competition may lower

wages and increase corporate profits, which in turn reduces property values due to the fact that

personal income is reduced. These negative changes in relationships often regard developed

markets like the U.S.

Fu and Ng (2001) states that the markets for securities generally are more efficient than

those of real estate due to rapid price adjustments when encountering new information whereas

real estate markets tend to prevent a rapid price adjustment. Furthermore, developed markets like

the U.S. tend to be negatively correlated as Ibbotson and Siegel (1984) explains. The researchers

found a negative correlation of -0.06 between U.S. real estate and the S&P 500 index. Their

research paper seeks to explore the commercial real estate returns and compare it to returns of

other assets. The annual data gathered between 1947 and 1982 shows the negative correlation. The

market-value weighted index is based on real estate values from the commercial, residential and

rural sectors. However the measurements made in the reasearch paper contain smoothing and

pricing inadequacies due to the estimated values it is based upon.

5

Other studies support the inverse relationship between real estate and stock markets in the

US, by using quarterly data in the time period 1977-1986 Hartzell (1986) valued the correlation

between the S&P 500 and real estate values to be negative - 0.25. Using quarterly data on a

slightly different time period, from 1980 to 1991, a negative correlation of - 0.0971 was found

between the Russell Index and real estate values in the US (Worzala and Vandell 1993). Geltner

(1993) altered the volatility of the real estate return index by correcting for the appraisal smoothing

that is applied to the Russell Index. The procedure resulted in a positive correlation of 0.3 between

real estate and stock markets.

Newer research conducted by Eichholtz and Hartzell (1996) documented the correlation to

be negative 0.09 between US real estate and stock markets. The data was gathered from the

Russell Index, quarterly and spanned from 1977 to 1993.

Numerous studies show that there is a correlation between the real estate markets and the

stock market returns. However, the given relationship between the two markets may be positively

correlated or inversely correlated; this varies from country to country. Quan & Titman (1997)

provides evidence of these relationships on an international level. The data used consisted of

capital value and rental indexes of prime office market properties for major cities in 17 countries

including the US between 1977 and 1994. They look at the capital and income returns as well as

total returns for the real estate. Both correlations between the property in each country and

correlations with the stock prices are analyzed. The performance comparison result in a wide

range of correlation coefficients, from negative - 0.79 to positive 0.886 for capital returns and

negative -0.821 to positive 0.999 for the income returns. These findings show positive

relationships between the real estate and stock markets in countries across the Asia-Pacific region

6

and some countries in Europe, whereas the relationship is less significant and more inversely

related in other such as the US, Canada, UK and Hong Kong.

According to the modern portfolio theory (Markowitz, 1959), investors may benefit from

diversifying their portfolios in asset classes that are less correlated than the ones that are positively

correlated as this will provide better diversification benefits.

Quan and Titman (1999) have also conducted research to examine whether stock prices and

real estate prices move together. They use the same data from their earlier research (1997) and find

that there are significant positive relationships between the real estate values and stock prices. The

underlying of their findings can be attached to the changes in the economic fundamentals; changes

in GDP in the countries impact the real estate values alongside the stock market returns. The

impact of inflation showed that real estate is a good alternative for long-term hedging but not on a

short term basis. The evidence provided states that there is a common factor. The findings of

positive correlation are in contrast with previous studies, this is partly due to the use of a larger

cross section and longer holding periods.

Bouchouicha and Ftiti (2012) analyzes the dynamic interactions that occur between the

macroeconomic environment and real estate markets in the US and UK in the aftermath of the

subprime crisis. Through applying a dynamic coherence function they measure the degree of

interaction between real estate market and the economy. Their findings show a degree of

synchronization between the UK and US in relationship with their respective economic

environments. In the long run the real estate market move alongside with the long term interest

rate, inflation rate and employment growth. In addition the results prove a clear linkage between

real estate and the monetary policy during crises; the real estate prices are considered a channel of

asset prices that the monetary authority uses to affect the economy. Moreover, wealth and housing

7

expenditure channels are conductive during real estate crises. The research paper indicates that

there are several macro environmental factors that may affect the real estate values.

Another interesting aspect is the investor’s psychological behavior. Investor sentiment may

affect the value of the stock markets. Studies in the past have been examining to what extent

investor sentiment may predict and affect returns.

Brown (1999) looks to identify unusual levels of individual investor sentiment that are

related to a higher level of volatility of closed-end investment funds. He states that if investor

sentiment is apparent, in the form of noisy signals, it may drive irrational investors affecting the

prices of financial assets. Furthermore the author explains that it has been debatable if uninformed

investors have any effect on the prices of different financial assets. The author describes a model

used in previous studies called DSSW noise-trader model that explains how traders investing on

non-fundamental information could affect prices in a systematic way. This model makes specific

testable predictions regarding the pricing of closed-end investment funds. The data stipulated was

from closed end funds and the sample period was from 1993 through 1994. Three volatility

estimates were calculated each day for every fund, daily volatility, closed-market volatility and

open market volatility. The results provide evidence that individual investor sentiment and

increased volatility in closed end funds is correlated. As volatility show systematic risk for

investors in closed end funds, the DSSW theory is supported by these findings.

This study opens for further research regarding investor sentiment and noise traders in

other asset classes. Brown (1999) states that research concentrating on risk and volatility may be

more beneficial than just examining the returns. In addition modifications in the measurement of

individual investor sentiment could improve the understanding of how and why uninformed traders

8

affect asset prices. An interesting question is how this can affect real estate value and the

relationship between corporate success and regional real estate prices.

Neal and Wheatley (1998) examine and measure the individual investor sentiment to find

out whether the sentiment measures can predict stock returns, size premium, and conditional on the

price variable. They state that the best time to long financial assets like stocks is when individual

investors show tendencies of bearish behavior; and contrary, the best time to sell stocks is when

they show a bullish behavior tendency. The method used is assessing the significance by

comparing test statistics to their empirical distributions, computed from randomization simulations

under the null hypothesis that returns are unpredictable. Three measures of investor sentiment are

used to predict the returns; level of discounts on closed end funds, ratio of odd-lot sales to

purchases and net mutual fund redemptions. The data used spans from 1933 to 1993. Results show

that discounts and net redemptions do in fact predict the size premium, but there was less

significance regarding the odd-lot ratio.

Springer (1996) examines the housing transactions for single-family houses in the real

estate market. He explores the sellers’ motivations, the price and how long the properties are on

the market. He states that there are several factors that can motivate the homeowner to sell their

property, these include relocation and financial distress. The outcome is that the list price will be

lower than the market price. This way the motivations of the seller impact the real estate prices and

the time the household is on the market. The data is collected from single-family homes in

Arlington TX between 1991 and 1993. The research findings result in that there are discounts for

houses with homeowners showing selling motivating behavior, and houses that have been

foreclosed or are vacant. Furthermore the list price is the mechanism used by the sellers, reducing

this will result in faster sales.

9

The above reviewed literature equips us with the understanding of determinants of housing

prices in relationship with stock prices and other macroeconomic factors in various countries.

Furthermore this research paper will contribute to the existing literature by explaining the

correlation between real estate prices and stock prices and if there are other macro environmental

factors that can affect the real estate indices.

CHAPTER 3: DATA DESCRIPTION

The empirical analysis of this paper has 158 observations and covers the time frame from

February 1st 2000 to March 31

st 2013. All data is collected on a monthly basis and is seasonally

adjusted. The data sample was obtained from the following sources: Bloomberg, S&P Dow Jones

Indices, Yahoo Finance, Bureau of Labor Statistics, Freddie Mac and Federal Reserve Economic

Data. During this time-period the great recession occurred between 2007 and 2009, therefore two

dummy variables will be included in the models; June 2006 - June 2009 and July 2009 - March

2013.

The data sample consists of the housing price indexes provided by Standard & Poor Dow

Jones Indices; Case Shiller 20 city index and each individual housing price index for a total of 15

cities. The reason that we chose to conduct the research only on 15 of the 20 Case Shiller city

indexes is due to the fact that we were unable to find sufficient S&P 500 companies headquartered

in the remaining cities. The dependent variables for the 20 city index and each individual

metropolitan area are presented in table 1. The desired national independent variables are

presented in table 2. Further the desired regional independent variables, the price weighted indexes

of each city are explained in table 3.

The dependent variable of the first model is the Case Shiller 20 city index. As stated above,

it was collected from the S&P Dow Jones Indices. This composite index measures the value of

residential real estate in 20 metropolitan areas of the United States. The index is published monthly

and is designed to be a reliable and consistent benchmark of housing prices in the United States. It

uses the Karl Case and Robert Shiller method of a house price index by using a modified version

of the weighted-repeat sales methodology (S&P Dow Jones Indices, 2013).

11

For the remaining 15 metropolitan areas, each individual city index is collected from the

S&P Dow Jones Indices as presented in table 1. They represent the housing price indices for every

metropolitan area. The indices are normalized to have a value of 100 in January 2000.

The independent variables in this study include: S&P500 index, UNEMP, CPI, INDPRO,

DISPPI, 15YMTGR, CONSENT, MSACSR and NHSUS. A brief description is available in table

2. The S&P500 index data was collected from the Bloomberg terminal. Moreover monthly data of

unemployment and the consumer price index (CPI) was made available by US Bureau of Labor

and Statistics. The remaining variables are all, with the exception of the 15 year mortgage rate,

collected from the Federal Reserve Economic Data (FRED).

The percentage change in CPI mirrors the inflation in the US. Industrial production index

(INDPRO) is chosen in favor of the GDP as it reflects the monthly output whereas GDP is

quarterly series and measures the market value. Disposable personal income per capita (DISPPI) is

included to measure individual’s ability to purchase goods/services and how this can impact the

housing prices. Furthermore the Freddie Mac 15 year mortgage rate (15YMTGR) is chosen due to

the fact that our study spans over the time period of the last 13 years. The consumer sentiment

index (CONSENT) of the University of Michigan is used due to its implications that can influence

the value of stocks. An interesting variable, the MSCSAR is a ratio of houses for sale to houses

sold, that draws a picture of the size of for sale inventory in relation to the number of houses

current being sold. Also included is the variable of new homes for sale in the US (NHSUS). This is

an indicator for new residential sales in units per month in America.

12

Since the values of the chosen data have large ranges between one another, we chose to use

the percentage change from month to month on all but the UNEMP, 15YMTGR and MSCAR

variables. This is done to get a more accurate feedback as the uneven figures tend to be smoothed.

Where:

- %∆X equals the monthly percentage change

- t2 equals successive month value

- t1 equals previous month value

The remaining three variables, (UNEMP, 15YMTGR & MSCAR) were left as is due to the

fact that the values were already presented in percentages.

CHAPTER 4: METHODOLOGY

This research paper studies the relationship between corporate performance, measured in

the change of stock prices, and the change in regional real estate prices. The main goal is to

determine if there is a significant correlation and whether the change in stock price can have an

effect on the real estate prices in the areas of where the companies are headquartered. We employ

the analysis tool that is Ordinary Lest Squares (OLS) regressions on our models, both on the 20

city index and for each of the 15 cities selected. Furthermore the study uses OLS regressions to

capture the significance of each individual variable by running simple regressions. However due to

the extent of these models, only the most significant cities will be documented in this paper.

∑ ( )

In the first model we undertake a multiple regression where we use house prices, presented

by the Case Shiller 20 city index as the dependent variable and S&P500, UNEMP, CPI, INDPRO,

DISPPI, 15YMTGR, CONSENT, MSACSR and NHSUS as the independent variables. Added are

two dummy variables for the time period of the great recession, between June 06 – June 09, and

the time period after, between July 09 – March 13.

∑ (

)

14

For the remaining 15 models describing each city, the same independent variables will be

used with small modifications; the exception of S&P500 and with the addition of a PWI for each

respectable city. The PWI explains the stock prices of the companies in the cities. Thus for the

simplicity of this study, we hereby use the abbreviation ‘VARX’ for the independent variables:

UNEMP, CPI, INDPRO, DISPPI, 15YMTGR, CONSENT, MSACSR, NHSUS. A more

explainable list of the models for each city is available in appendix 1.

In model 2 we investigate the outcome of using the house prices in Atlanta metropolitan

area, the same national independent variables with the exception of S&P500. Instead we use a

regional independent variable, PWI AT-GA, consisting of the stock prices in the respective area.

∑ ( )

Model 3 uses the same concept for the greater Boston area. PWI BO-MA is the index for the stock

prices in Boston.

∑ ( )

Model 4: Greater Cleveland, Ohio.

∑ ( )

15

Model 5: Chicago metropolitan area, Illinois.

∑ ( )

Model 6: Charlotte, North Carolina.

∑ ( )

Model 7: Dallas, Texas.

∑ ( )

Model 8: Denver, Colorado.

∑ ( )

Model 9: Los Angeles, California.

∑ ( )

Model 10: Miami, Florida.

∑ ( )

16

Model 11: Minneapolis, Minnesota.

∑ ( )

Model 12: New York City, New Jersey.

∑ ( )

Model 13: Phoenix, Arizona.

∑ ( )

Model 14: Seattle, Washington.

∑ ( )

Model 15: San Francisco, California.

∑ ( )

Model 16: Washington D.C.

17

∑ ( )

CHAPTER 5: DISCUSSION AND RESULTS

Model 1: Case Shiller 20 city index

Firstly we ran the model for the CS 20 city index vs. all the variables, the outcome is

available in table 4. The reason for focusing on the multiple regression for the 20 city index is to

see if the variables have a combined impact on the changes in real estate prices in the 20 cities

involved. It gives a generally more accurate indication than simple regressions.

Investigating the results closer, it shows an R squared of 0.831. This tells us that the model

fits our data as the data is in a close range of the regression line. The results from this model

explains that UNEMP, INDPRO, MSCASR and NHSUS are statistically significant at the 1% and

10% significance level, respectively. UNEMP and INDPRO had a positive coefficient, which can

explain that if unemployment and US industrial production increases it would explain the increase

in housing prices by respectively 0.1 and 14.2%. Moreover MSCASR and NHSUS shows a

negative trend if the time a house on the market is increased or if an additional lot of new homes

are built, the change in the index will be negative. The two dummy variables created show that the

period of June 2006 - June 2009, and July2009 – March 2013 are different than the 2000 – 2006

period. Both have a negative coefficient.

Model 2 – 16, Individual cities.

In the second model we apply the same technique, where we test the independent variables

against the real estate index of the metropolitan area. Instead of using the S&P500 index we add

the PWI AT-GA. The R square is 0.350 which is lower than the R square for the whole 20 city

index which is a weaker relationship. The only significant variable is MSCASR, at 1 %

19

significance level. The dummy variable for the second period has a positive coefficient which

states that this time period was different than the 2000-2006 period.

In Boston the results are slightly different. R squared is 0.593, PWI is significant at the

10% level, UNEMP, 15YMTGR and MSCASR are statistical significant at the 1% level and CPI

shows significance at 5%. As the change in stock price (PWI) is significant, the coefficient is

negative. Thus it explains when the value of the stock index increases in Boston, the real estate

value decreases by -1.2%. Furthermore, an increase in unemployment, inflation and mortgage rate

increases housing prices by 0.2, 21.9 and 0.3 % correspondingly.

Cleveland boasted an R square of 0.243, this shows a weak relationship between the

variables. The significant variables were UNEMP and MSCAR. Again, a positive shift, of 0.2 %

for the real estate price following an increase in unemployment. The MSCAR articulates, as

expected a negative fall in housing prices as the monthly supply of homes in the US increases.

Chicago results showed an R squared of 0.589, this indicates a relatively good relationship.

INDPRO and MSCASR are the only significant variables, both at the 1% level.

Charlotte, NC has a weaker relationship than Chicago whit an R squared of 0.467 to prove

that. Furthermore an interesting amount of variables shows a significance. UNEMP and MSCASR

are both significant at 1%, INDPRO at 5% and 15YMTGR at 10%. In contrast to Boston,

unemployment and the interest rate here has a negative coefficient. Thus an increase would result

in a decrease of 0.1 % in housing prices. Both dummy variables are significant, saying that the

2006-2009 and 2009-2013 period were different than the 2000-2006 period.

The R square for Dallas is 0.290. PWI has a p-value of 0.168 which isn’t significant at our

tested level, but it does however draw a picture. Running a simple regression between the SP DA-

TX and PWI shows that the PWI is significant at the 5% level. This indicates that when an increase

20

in the stock index occurs, the housing prices increases as well. The great recession dummy variable

articulates that the period of 2006 - 2009, was different than the period of 2000-2006. The

coefficient is positive.

Denver, Colorado; R squared of 0.490 indicates a relationship. There are two significant

variables for this area, 15YMTGR and MSCASR. Both significant at the 1 % level. The interest

rate has a positive coefficient, this articulates an increase in housing prices at the increase of

interest rates. MSCASR is as in the other models negative. Dummy variable for 2009-2013 period

is significant.

Los Angeles shows a strong positive relationship with an R square of 0.780. UNEMP and

MSCASR are both significant at 1% level, while NHSUS is significant at 5%. INDPRO shows

significance at the 10% level. An increase in unemployment and industrial production increases

the housing prices by 0.4%, and 16.3%, moreover an increase in houses on the market and new

homes built and sold, will decrease the price respectively by 0.5 % and 1.2% As in Denver the

2009-2013 period is significant, indicating this period differs from the 2000-2006 period.

Miami shows a decline in real estate prices. With an R square of 0.832 it shows a strong

relationship between the variables. INDPRO was the only significant variable with a positive

coefficient. It was significant at the 5 % level. The results explained that when industrial

production increased, housing prices would increase by 21%. Furthermore DISPPI, 15YMTGR

and MSCASR were significant at 10 %, 5 % and 1 % levels, respectively. These however were

negative in their coefficient, meaning housing prices would drop with an increase in disposable

income, interest rate and supply of houses on the market. The results also states that the great

recession was a downturn in real estate prices, and the downfall continued after 2009 until the

present.

21

Minneapolis gives an R squared of 0.641. Significant variables count as INDPRO,

15YMTGR and MSCASR. These are significant at the levels 5 and 1 %. Where in Miami an

increase in interest rate gave a decrease in housing prices, here it is the opposite. The results tell

that housing prices would increase by 0.3 %. INDPRO and MSCASR give effects as expected.

New York. This metropolitan area and its real estate prices are affected by changes in stock

prices. An R square of 0.792 indicates a positive relationship between the variables. The PWI is

significant at 5 % level with a negative coefficient. This articulates that for every increase in PWI,

there is a fall in real estate prices of – 0.8 %. In addition INDPRO, has a positive relationship at the

5 % level, whereas MSCASR is negative at 1 %.

Phoenix is another downturn in terms of real estate. The relationship between the variables

is defined as strong as the 0.741 R squared dictates. The PWI is significant at the 10 % level and

indicates that an increase in the stock prices will see the real estate prices drop by 2 %. In addition

15YMTGR and MSCASR, both significant at the 1 % level, will affect the real estate negatively.

Moreover, an increase in CPI (inflation) and INDPRO will see the prices in real estate rise.

Significant at 10 % and 1 % respectively. The dummy variable for the time period 2006-2009 is

significant, this states that the great recession period was different than the one spanning between

2000 and 2006.

Seattle shows an R square of 0.697, which articulates a strong relationship in this area as

well. PWI is significant at the 5 % level and explains that for an increase in the stock prices,

housing prices will increase by 1 %. INDPRO is significant at 1 % level. Unemployment has a

negative effect on the housing market, an increase will decrease real estate prices by – 0.3 %. It is

significant at the 1 % level. In addition, 15YMTGR and MSCASR are significant at the 1 % level

22

with negative coefficients stating that when interest rates and amount of houses on sale increases,

the housing prices will decrease by – 0.2 % and – 0.2 % respectively.

San Francisco is another hi-tech city with results able to identify variables’ relationships

with housing prices. The R square is 0.641 which indicates a strong relationship. Even though the

PWI is not significant at the tested levels and it has a P-value of 0.171, it gives an indication that

the real estate prices and stock prices are correlated. By running a simple regression on SP SF-CA

vs. PWI SF-CA, the PWI proves to be significant at the 5 % level. Furthermore UNEMP, INDPRO

and 15YMTGR are significant at the 1 % level, and they all seem to have a positive impact on the

housing prices. When unemployment, industrial production and interest rate increases, housing

prices in this area tend to increase with 0.4 %, 40 % and 0.5 % respectively. Moreover MSCASR

is 1 % significant and has a negative outcome on the housing prices of – 0.6 %.

Washington D.C., the last multiple regression model gave good results. The R square of

0.766 indicates a strong positive relationship between variables. Moreover the PWI is significant at

the 10 % level. It has an impact on real estate of 1.5 % when the price of stocks increase. UNEMP

is significant at the 1 % level where as INDPRO is significant at the 10 % level. Both with a

positive coefficient explaining an increase would make real estate prices increase by 0.3 % and

12.1 % respectively. MSCASR is continuing to show a negative effect, and is significant at the 1

% level. The two dummy variables for 2006-2009 and 2009-2013, are significant and show a

difference in comparison to the 2000-2006 period.

CHAPTER 6: CONCLUSION

This study set out to capture the sensitivity of the real estate prices in selected areas of the

Case Shiller 20 city index, in correlation to changes in corporate performance presented in form of

stock price fluctuations in the respective areas. A total of 15 cities, in addition to the 20 city index

was tested. The corporate performance was of large S&P500 companies that were headquartered in

selected cities. Number of national independent variables for the 20 city index model was 9,

including S&P500 index. For the remaining 15 cities the S&P500 variable was replaced with a

price weighted index made out of 4 companies from each city.

The empirical results were helpful in understanding and answering the research question on

whether housing prices can be linked to the performance of larger companies that are

headquartered in those respective areas. The results supports the notion that in some metropolitan

areas the corporate performance is of significance to the change in real estate value.

For the Case Shiller 20 city index corporate performance proved not to be as correlated as

expected. Other explanatory variables showed to be of a higher significance; industrial production,

unemployment and the average monthly supply of houses for sale versus houses sold. This

explains that this housing index is more affected by how much the US industry produces, the house

for sale/houses sold ratio and whether the unemployment rate increases.

For the individual studies of every city, the outcome turned out to be various. The hi-tech

city of Seattle proved to be positively correlated with the corporate performance as the results

showed. Moreover Washington D.C. proved to be positively correlated with the business

performance. Dallas also showed a positive development.

Looking at the negative correlated models, results show that housing prices in Phoenix,

New York and Boston were negatively correlated with corporate performance. In addition to the

24

corporate performance, various national variables tend to play an important role in influencing

housing prices.

Some findings were however less explainable, in a number of the cities unemployment and

real estate prices tended to be positively correlated, whereas in others it was negatively correlated.

As an ending conclusion, the results and findings support the research question; thus, the null

hypothesis cannot be rejected.

Future research regarding house prices is to look at investor sentiment in the individual

cities. Previous studies have been looking into investor sentiment and noise traders for stocks, it

could open for research in other asset classes as real estate. Moreover, risk and volatility may be

more beneficial than just examining the returns of companies. In addition, it would be beneficial

to gather data and analyze how long houses are on the market and survey sellers and buyers’

motivation and whether this can have a substantial impact on real estate prices.

TABLES

Table 1. Dependent Variables, variable names and description

ABBREVIATION DESCRIPTION

S&P C-S 20 Standard & Poor’s Case

Shiller 20 city index

S&P AT-GA City index for Atlanta,

Georgia

S&P BO-MA City index for Boston,

Massachusetts

S&P CE-OH City index for Cleveland,

Ohio

S&P CH-IL City index for Chicago,

Illinois

S&P CR-NC City index for Charlotte,

North Carolina

S&P DA-TX

City index for Dallas, Texas

S&P DN-CO City index for Denver,

Colorado

S&P LA-CA City index for Los Angeles,

California

S&P MI-FL

City index for Miami, Florida

S&P MN-MI City index for Minneapolis,

Minnesota

S&P NY-NJ City index for New York,

New Jersey

S&P PHX-AZ City index for Phoenix,

Arizona

S&P SE-WA City index for Seattle,

Washington

S&P SF-CA City index for San Francisco,

California

S&P W-DC City index for Washington,

District of Columbia

26

Table 2. Independent National Variables, variable names and description

ABBREVIATION DESCRIPTION

S&P500 Standard and Poor 500 Stock

Index

UNEMP Unemployment rate

CPI Consumer Price Index

INDPRO Industrial Production

DISPPI Disposable Personal Income

15YMTGR 15 Year Mortgage Rate

CONSENT Consumer Confidence

Sentiment Index

MSACSR Monthly Supply of Homes in

the US Ratio

NHSUS New Homes Sold in the US

27

Table 3. Independent Regional Variables, variable names and description

ABBREVIATION DESCRIPTION STOCKS

PWI AT-GA Price Weighted Index

Atlanta, created from 4

S&P500 companies

United Parcel Service (UPS),

Coca Cola Co. (KO), Southern

Co. (SO) and Sun Trust Banks

(STI)

PWI BO-MA Price Weighted Index

Boston, created from 4

S&P500 companies

American Tower Corp. (AMT),

Boston Scientific (BSX), State

Street Corp. (STT) and Ironic

Mountain Inc. (IRM)

PWI CE-OH Price Weighted Index

Cleveland, created from 4

S&P500 companies

Cliffs Natural Resources (CLF),

Eaton Corp. (ETN), Parker-

Hannifin (PH) and Sherwin-

Williams (SHW)

PWI CH-IL Price Weighted Index

Chicago, created from 4

S&P500 companies

Boeing (BA), Exelon (EXC),

Abbott Laboratories (ABT) and

Northern Trust Corp. (NTRS)

PWI CR-NC Price Weighted Index

Charlotte, created from 4

S&P500 companies

Duke Energy (DUK), Nucor

Corp. (NUE), Bank of America

(BAC) and Family Dollar Stores

(FDO)

PWI DA-TX Price Weighted Index

Dallas, created from 4

S&P500 companies

AT&T Inc. (T), Southwest

Airlines (LUV), Texas

Instruments (TXN) and Tenet

Healthcare Corp. (THC)

PWI DN-CO Price Weighted Index

Denver, created from 4

S&P500 companies

Da Vita (DVA)

Molson Coors Brew Co. (TAP)

Newmont Mining Corp. (NEM)

Apartment Investment & Mgmt.

(AIV)

PWI LA-CA Price Weighted Index Los

Angeles, created from 4

S&P500 companies

Occidental Petroleum (OXY)

Healthcare Property Investors

(HCP)

Macerich co. (MAC)

Mattel Inc. (MAT)

28

Table 3. Independent Regional Variables, variable names and description cont’d

ABBREVIATION DESCRIPTION STOCKS

PWI MI-FL Price Weighted Index

Miami, created from 4

S&P500 companies

Carnival Corp (CCL)

Lennar Corp. (LEN)

Ryder System (R)

Auto Nation Inc. (AN)

PWI MN-MI Price Weighted Index

Minneapolis, created from 4

S&P500 companies

Target (TGT)

Ecolab Inc. (ECL)

US Bancorp. (USB)

Xcel Energy (XEL)

PWI NY-NJ Price Weighted Index New

York, created from 4

S&P500 companies

American Intl. Group (AIG),

Goldman Sachs (GS)

Verizon Comm. (VZ) JPMorgan

Chase (JPM)

PWI PHX-AZ Price Weighted Index

Phoenix, created from 4

S&P500 companies

Apollo Group Inc. (APOL)

Freeport-McMoran Copper &

Gold (FCX)

Republic Service (RSG)

PetSmart (PETM)

PWI SE-WA Price Weighted Index

Seattle, created from 4

S&P500 companies

Amazon Inc. (AMZN)

Starbucks Corp. (SBUX)

Nordstrom Inc. (JWN)

Expeditors Intl. (EXPD)

PWI SF-CA Price Weighted Index San

Francisco, created from 4

S&P500 companies

Wells Fargo (WFC)

McKesson (MCK)

Gap (GPS)

PG&E Corp. (PCG)

PWI W-DC Price Weighted Index

Washington D.C, created

from 4 S&P500 companies

Washington Post (WPO)

Danaher Corp. (DHR)

Pepco Holdings (POM)

AvalonBay Communities Inc.

(AVB)

29

Table 4. Multiple regression SP CS 20

VARIABLE NAMES

S&P CASE SHILLER 20 CITY INDEX

Coefficients P-value

Intercept 0.012 0.007***

S&P 500 0.011 0.231

UNEMP 0.001 0.003***

CPI 0.093 0.290

INDPRO 0.142 0.007***

DISPPI -0.030 0.420

15YMTGR 0.001 0.188

CONSENT 0.000 0.985

MSACSR -0.003 0.000***

NHSUS -0.006 0.073*

T: Jun06-Jun09 -0.004 0.042**

T: Jul09-Mar13 -0.005 0.003***

R squared 0.831

* = 10% significance ** = 5% significance *** = 1% significance

30

Table 5. Summary multiple regressions 15 cities

VARIABLE NAMES ATLANTA

BOSTON

CLEVELAND

Coefficients P-value Coefficients P-value Coefficients P-value

Intercept 0.018 0.026** -0.010 0.051* -0.004 0.518

PWI 0.007 0.625 -0.012 0.052* 0.007 0.359

UNEMP -0.001 0.140 0.002 0.000*** 0.002 0.009***

CPI 0.091 0.588 0.219 0.035** 0.038 0.791

INDPRO 0.079 0.432 -0.012 0.845 -0.084 0.324

DISPPI -0.020 0.782 -0.008 0.848 -0.043 0.483

15YMTGR 0.000 0.731 0.003 0.000*** 0.001 0.174

CONSENT 0.003 0.786 -0.003 0.727 0.000 0.994

MSACSR -0.002 0.001*** -0.003 0.000*** -0.003 0.000***

NHSUS -0.005 0.494 -0.004 0.368 0.000 0.938

T: jun06-jun09 0.003 0.439 0.001 0.671 0.005 0.124

T: jul09-Mar13 0.006 0.047** -0.001 0.734 -0.002 0.452

R squared 0.350 0.593 0.243

* = 10% significance ** = 5% significance *** = 1% significance

Table 5. Cont’d

VARIABLE NAMES CHICAGO

CHARLOTTE

DALLAS

Coefficients P-value Coefficients P-value Coefficients P-value

Intercept 0.019 0.003*** 0.022 7.2E-07*** 0.001 0.825

PWI -0.009 0.374 0.003 0.702 0.008 0.168

UNEMP -0.001 0.282 -0.001 0.003*** 0.001 0.211

CPI 0.001 0.993 0.145 0.106 0.143 0.156

INDPRO 0.204 0.009*** 0.112 0.037** 0.043 0.468

DISPPI 0.020 0.716 0.040 0.293 -0.004 0.923

15YMTGR 0.000 0.891 -0.001 0.077* 0.001 0.104

CONSENT -0.003 0.780 0.004 0.498 0.003 0.715

MSACSR -0.002 0.000*** -0.002 3.5E-05*** -0.002 0.000***

NHSUS -0.002 0.767 -0.005 0.187 0.002 0.579

T: jun06-jun09 -0.003 0.246 0.005 0.011** 0.006 0.005***

T: jul09-Mar13 -0.001 0.685 0.004 0.023** 0.003 0.186

R square 0.589 0.467 0.293

* = 10% significance ** = 5% significance *** = 1% significance

31

Table 5. Cont’d

VARIABLE NAMES DENVER

L.A

MIAMI

Coefficients P-value Coefficients P-value Coefficients P-value

Intercept -0.001 0.728 0.015 0.033 0.039 2.2E-08***

PWI 0.008 0.245 -0.002 0.787 -0.007 0.351

UNEMP 0.000 0.740 0.004 0.000*** 0.001 0.470

CPI 0.082 0.344 0.078 0.581 0.082 0.543

INDPRO -0.069 0.185 0.163 0.056* 0.210 0.010**

DISPPI -0.020 0.585 -0.053 0.381 -0.106 0.064*

15YMTGR 0.002 0.000*** 0.000 0.921 -0.002 0.032**

CONSENT 0.006 0.353 0.003 0.748 0.014 0.160

MSACSR -0.002 0.000*** -0.005 0.000*** -0.004 9.2E-11***

NHSUS 0.002 0.568 -0.012 0.040** -0.005 0.330

T: jun06-jun09 0.003 0.107 -0.005 0.116 -0.012 9.7E-05***

T: jul09-Mar13 0.007 0.000*** -0.012 0.000*** -0.010 0.000***

R Square 0.490 0.780 0.832

* = 10% significance ** = 5% significance *** = 1% significance

Table 5. Cont’d

VARIABLE NAMES MINNEAPOLIS N.Y PHOENIX

Coefficients P-value Coefficients P-value Coefficients P-value

Intercept 0.002 0.817 0.013 0.001*** 0.058 1.5E-08***

PWI 0.002 0.867 -0.008 0.034** -0.020 0.089*

UNEMP 0.001 0.179 0.000 0.623 -0.001 0.226

CPI 0.234 0.116 0.112 0.171 0.332 0.092*

INDPRO 0.192 0.031** 0.101 0.043** 0.357 0.003***

DISPPI -0.018 0.775 -0.039 0.267 -0.028 0.739

15YMTGR 0.003 0.002*** 0.000 0.486 -0.003 0.005***

CONSENT 0.016 0.151 -0.002 0.675 0.018 0.190

MSACSR -0.004 0.000*** -0.001 0.000*** -0.005 1.6E-07***

NHSUS -0.007 0.226 -0.003 0.404 -0.010 0.215

T: jun06-jun09 0.003 0.338 -0.009 0.000*** -0.008 0.062*

T: jul09-Mar13 0.007 0.015** -0.008 0.000*** 0.000 0.965

R square 0.641 0.793 0.741

* = 10% significance ** = 5% significance *** = 1% significance

32

Table 5. Cont’d

VARIABLE NAMES SEATTLE

SFO

W. D.C

Coefficients P-value Coefficients P-value Coefficients P-value

Intercept 0.043 1.8E-14*** -0.013 0.219 0.008 0.175

PWI 0.010 0.034** 0.022 0.171 0.015 0.072*

UNEMP -0.003 9.8E-07*** 0.004 0.001*** 0.003 0.000***

CPI 0.092 0.375 0.234 0.262 0.184 0.116

INDPRO 0.275 1.6E-05** 0.400 0.002*** 0.121 0.083*

DISPPI -0.047 0.286 -0.073 0.410 -0.020 0.691

15YMTGR -0.002 0.001*** 0.005 0.000*** 0.000 0.684

CONSENT -0.001 0.864 0.000 0.977 0.005 0.560

MSACSR -0.002 4.2E-05*** -0.006 0.000*** -0.003 0.000***

NHSUS -0.002 0.614 0.003 0.725 -0.007 0.139

T: jun06-jun09 0.000 0.868 0.006 0.156 -0.008 0.002***

T: jul09-Mar13 0.002 0.300 0.004 0.317 -0.013 0.000***

R square 0.698 0.641 0.766

* = 10% significance ** = 5% significance *** = 1% significance

BIBLIOGRAPHY

Bjørnland, H.C. & Jacobsen, D.H. (2012) House Prices and Stock Prices: Different Roles in the

U.S. Monetary Transmission Mechanism. Available at:

http://www.bi.no/InstitutterFiles/Samfunns%C2%B0konomi/CAMP_wp_012012.pdf

[Accessed: 10 November, 2013]

Bloomberg Database (2013) Accessed at UNCW.

Bouchouicha, R. and Ftiti, Z. (2012) Real Estate Markets and the Macro economy: A Dynamic

Coherence Framework. Economic Modeling, 29(5) pp.1820-1829

Brown, G., 1999, Volatility, Sentiment, and Noise Traders, Financial Analysts Journal, 55(2)

pp.82-90

Bureau of Labor Statistics (2013) Unemployment Rate. Available at:

http://data.bls.gov/pdq/SurveyOutputServlet [Accessed: 10 September, 2013]

Burfisher, M.E., Robinson, S. & Thierfelder, K. (2001) The Impact of NAFTA on the United

States. Journal of Economic Perspectives, 15(1) pp. 125-144.

Dao, M. & Loungani, P. (2010) The Human Cost of Recessions: Assessing it, Reducing it.

Available at: http://www.imf.org/external/pubs/ft/spn/2010/spn1017.pdf [Accessed: 10

November, 2013]

Eichholtz, P.M.A. and Hartzell, D.J. (1996) Property Shares, Appraisals and the Stock Market:

An International Perspective. Journal of Real Estate Finance and Economics, 12(2)

pp.163-178.

FRED (2013a) Industrial Production Index Available at:

http://research.stlouisfed.org/fred2/series/INDPRO/downloaddata?rid=13&soid=1

[Accessed: 08 September, 2013]

34

FRED (2013b) Monthly Supply of Homes in the United States. Available at:

http://research.stlouisfed.org/fred2/series/MSACSR [Accessed: 10 September, 2013]

FRED (2013c) University of Michigan: Consumer Sentiment. Available at:

http://research.stlouisfed.org/fred2/series/UMCSENT/ [Accessed: 10 September, 2013]

FRED (2013d) Consumer Price Index for All Urban Consumers: Housing. Available at:

http://research.stlouisfed.org/fred2/series/CPIHOSSL/downloaddata?cid=32416

[Accessed: 10 September, 2013]

FRED (2013e) Disposable Personal Income: Per Capita. Available at:

http://research.stlouisfed.org/fred2/series/A229RX0 [Accessed: 10 September, 2013]

FRED (2013e) New Homes Sold in the United States. Available at:

http://research.stlouisfed.org/fred2/series/HSN1FNSA/downloaddata?cid=32426

[Accessed: 10 September, 2013]

Freddie Mac (2013) 15 year fixed rate mortgages. Available at:

http://www.freddiemac.com/pmms/pmms15.htm [Accessed: 10 September, 2013]

Fu,Y. and Ng, L.K. Market Efficiency and Return Statistics: Evidence from Real Estate and Stock

markets using a Present-Value Approach. Real Estate Economics, 29(2) pp.227-250

Geltner, D., 1993. Estimating Market Values from Appraised Values without Assuming an

Efficient Market. Journal of Real Estate Research, 8 (3) pp. 325 – 345.

Gould, D.M. (1998) Has NAFTA Changed North American Trade? Economic Review. pp. 12.

Hartzell, D., (1986). Real Estate in the Portfolio. The Institutional Investor: Focus on Investment

Management, edited by F.J. Fabozzi, Cambridge, MA: Ballinger.

Ibbotson, R., and L. Siegel, (1984). Real Estate Returns: A Comparison with Other Investments.

AREUEA Journal,12 (3) pp. 219 – 241.

35

Kliesen, K.L. (2003) The 2001 Recession: How Was It Different and What Developments May

Have Caused It. Review (00149187) 85(5) pp. 23-37.

Knoop, T.A. (2010) Recessions and Depressions: Understanding Business Cycles. 2nd edn. Santa

Barbara: Praeger.

Markowitz, H.M. (1959) Portfolio Section. Efficient Diversification of Investment, New Haven,

CT, Yale University Press.

NBER (2010) US Business Cycle Expansions and Contractions. Available at:

http://www.nber.org/cycles/cyclesmain.html [Accessed: 10 November, 2013]

Neal, R. and S. Wheatley, 1998, Do Measures of Investor Sentiment Predict Returns? Journal of

Financial and Quantitative Economics, 33(4) pp.523-547

Quan, D.C. and Titman, S. (1997) Commercial Real Estate Prices and Stock Market Returns: An

International Analysis. Financial Analyst Journal, 53(3) pp. 21-35

Quan, D.C. and Titman (1999), S. Do Real Estate Prices and Stock Prices Move Together? An

International Analysis. Real Estate Economics, 27 (2) pp.183-207

S&P Dow Jones Indices (2013a) S&P/Case-Shiller 20-City Composite Home Price Index.

Available at: http://us.spindices.com/indices/real-estate/sp-case-shiller-20-city-composite-

home-price-index [Accessed: 5 September, 2013]

S&P Dow Jones Indices (2013b) S&P/Case-Shiller Home Price Indices: Methodology. Available

at: http://www.spindices.com/documents/methodologies/methodology-sp-cs-home-

price-indices.pdf [Accessed: 01 November, 2013]

Schulz, R. & Werwatz, A. (2004) A State Space Model for Berlin House Prices: Estimation and

Economic Interpretation. Journal of Real Estate Finance and Economics, 1(28) pp. 37-57

36

Springer, T.M. (1996) Single-Family Housing Transactions: Seller Motivations, Price, and

Marketing Time. Journal of Real Estate Finance and Economics, 13 (3) pp.237-254

Tse, R.Y.C. (2001) Impact of Property Prices on Stock Prices in Hong Kong. Review of Pacific

Basin Financial Markets and Policies, 4 (1) pp. 29-43.

Walsh C.E. (1993) What Caused the 1990-1991 Recession. Economic Review of the Federal

Reserve Bank of San Francisco, pp. 34-38

Wilkerson, C.R. (2009) Recession and Recovery Across the Nation: Lessons from History.

Economic Review. 94(2) pp. 5-24.

Worzala, E., and K. Vandell. (1993). International Direct Real estate Investments as Alternative

Portfolio Assets for Instituitional Investors: An Evaluation. Paper presented at the 1993

AREUEA meetings, Anaheim, CA.

Yahoo Finance (2013) Available at: http://finance.yahoo.com/

APPENDIX

Appendix 1. MODEL 2 AT-GA:

∑ (

)

MODEL 3 BO-MA:

∑ (

)

MODEL 4 CE-OH:

∑ (

)

MODEL 5 CH-IL:

∑ (

)

MODEL 6 CR-NC:

∑ (

)

38

MODEL 7 DA-TX:

∑ (

)

MODEL 8 DN-CO:

∑ (

)

MODEL 9 LA-CA:

∑ (

)

MODEL 10 MI-FL:

∑ (

)

MODEL 11 MN-MI:

∑ (

)

MODEL 12 NY-NJ:

∑ (

)

39

MODEL 13 PHX-AZ:

∑ (

)

MODEL 14 SE-WA:

∑ (

)

MODEL 15 SF-CA:

∑ (

)

Model W-DC:

∑ (

)