gatton.uky.edugatton.uky.edu/sites/default/files/parkistan-project/Muha… · Web viewMONETARY POLICY AND HOUSING PRICES BOOMS IN US: WHAT CONTRIBUTE THE MOST NOMINAL OR REAL INTEREST

MONETARY POLICY AND HOUSING PRICES BOOMS IN US: WHAT

CONTRIBUTE THE MOST NOMINAL OR REAL INTEREST RATES.

Submitted by

Muhammad Tariq Post Doc Fellow Gatton College of Business and Economics, University of

Kentucky, US.

Submitted to

Christine Hankins, Associate Professor of Finance Gatton College of Business and Economics,

University of Kentucky, US.

Dedication

Thanks to Allah the almighty and supreme power of the universe that today I have atleast this

first draft of my research project.

First of all I am extremely thankful to the Abdul Wali Khan University Mardan Pakistan

specifically the worthy and dynamic Vice Chancellor Professor Dr. Ihan Ali Khan for giving me

this great opportunity to visit University of Kentucky, USA one of the few leading university of

the world.

At the same time I am also grateful to the US government for providing this golden opportunity

to the faculty members from KP universities Pakistan. Many thanks to Professor Johnson Nancy

for his continuous and consistent support to our entire faculty members at all forums. I really

pray for you and your family happy and long lives.

I am also very much thankful to Christine Hankins whose lectures, meetings and specifically her

standard research articles really become a source of motivation for me to complete this project.

I really enjoyed my stay at the university of Kentucky and will try my best to utilize what I have

learned from all the intellectual professors of university of Kentucky.

Finally, I would like to say thanks to Andrea who made our journey very pleasant for us by

supporting us to visit beautiful places in Kentucky.

1

MONETARY POLICY AND HOUSING PRICES BOOMS IN US: WHAT

CONTRIBUTE THE MOST NOMINAL OR REAL INTEREST RATES.

Abstract

The purpose of the present study is examine the role of the monetary policy in US housing prices

boom. Specifically, it is whether the US housing prices rise is the result of the nominal or real

interest rate fluctuations. Monthly data for the period January-2000 to December- 2015 has been

used for the empirical estimation of the results. Hodric Prescott Filter method has been used for

the stationarity of the data. Moreover, Ordinary Least Squares method, Granger Causality test

and Vector Auto Regression has been applied for the computation of the results.

Overall the main findings of the study are that nominal interest alongwith inflation and

population growth rate does contribute to the US housing Price rise. Whereas, the relationship

between the real interest rate and housing price index seems to be ambiguous. It is suggested that

the Federal Reserve can used nominal interest as an effective tool for controlling the US housing

price boom.Keywords: Housing Price Index, HP filter method, Vector Autoregression

1. IntroductionHousing sector is considered one of the important sectors of the economy. It plays a key role in

the economic activity of a country and the central banks around the world uses the benchmark

lending rate / interest rate as a monetary policy tool to regulate the economic activities. About

250 ancillary industries, such as cement, steel, brick, timber and building materials, are

dependent on the real estate industry (Singh, 2015). The housing sector plays a major role in

economic growth and stabilization through the creation of jobs in construction and materials and

demand for financial services. It also has the potential of absorbing a large number of skilled and

unskilled workforce, significantly reducing unemployment and, thereby, reducing poverty in the

country. This attribute of housing, coupled with its size and its multiplier effect on the economy,

gives it the role of a leading indicator of the imminent state of health of the economy at large

(The Ministry of Commerce, Government of Pakistan, 2006).

Housing may be considered an ordinary part of a household’s everyday life. However, it can also

be a rather complex economic phenomenon, as dwellings can have several functions. Apart from

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being the traditional ‘roof over one’s head’, a house can serve as a source of wealth

accumulation, a valuable item for bequest motives, or a form of investment. Another distinctive

characteristic of housing is its sizeable share in household wealth, implying its importance in the

household’s decision-making process. As a result, shocks to the housing market can have a

significant impact on household behavior and on the economy as a whole (Kiss and Vadas,

2005).

Housing construction activity and productivity has been rising in Pakistan in recent years from

very low levels. However, the housing sector in Pakistan is still in its infancy when compared

with other developing and developed countries. There is a tremendous potential for growth in

Pakistan’s economy, given a relatively stable and growing economy, a rapidly increasing

population, unmet housing demand, and a growing awareness of housing finance options. There

is a need to have a well-developed housing finance market/system to help the housing sector. In

Pakistan, housing finance market is still in its nascent stage compared to other countries. At

present, twenty four commercial banks, House Building Finance Company Limited (HBFCL)

and one microfinance bank are catering to the housing finance needs (State Bank of Pakistan,

2015).

Housing price fluctuations affect private consumption as these tend to generate wealth effects

which in turn affect households’ lifetime wealth and consequently determine their spending and

borrowing plans. According to the (Boone and Girouard, 2002), housing wealth accounts for a

large share of household wealth and in some countries is the principal component of total wealth,

so the wealth effect of house price movements is expected to be stronger than that of any other

financial asset. For this reason, housing wealth may cause a higher marginal propensity to

consume than stock market wealth does (Ludwig and Slok, 2004). Furthermore, rising housing

prices tend to stimulate residential investment, thereby increasing economic activity, while

increasing real estate prices lead to increases of the value of firms’ and households’

collateralizable assets, affecting their ability to borrow and to further finance business investment

and consumption (Girouard and Blondal, 2001), with this increase in credit demand affecting

bank lending. Moreover, rising housing prices increase both banks property’s value as well as the

value of loans secured by housing collateral. Consequently, the transmission of monetary policy

may also be influenced by the housing market via its effect on the cost of capital, housing supply

and demand, households’ wealth and bank credit (Mishkin, 2007).

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On contrary, the recent boom in housing prices observed in most of the developed countries

refreshed the debate that “asset prices can and do run wild at rates capable of negative effects on

real economic activity1”. The rise in the US house prices and also in other parts of the world

since 2000 has been described by The Economist magazine2 as the “biggest bubble in history”.

Since 2000 the Economist has estimated that the value of residential property in developed

countries rose by more than $30 trillion. This sharp upward tendency in housing prices has

several economic effects working through both the real and nominal sides of the economy. When

the housing prices are rising, this results in substantial increase in investment in new houses lead

to more job opportunities and economic activities in housing market but less activity in other

sectors of the economy. Similarly, when there is a rise in house prices, this increase household

wealth and consumption in the housing market against the other sectors affecting the economy.

This boom and bust in the housing prices and its adverse consequences for the economies

renewed the interest of the researchers in the debate whether the central banks should response to

it or not? The subject is debatable in the literature. Some economists’ mentioned that central

banks should only focus on its key policy goals of price stability and output growth (See,

Bernanke and Gertler,2001). However, the opposed view is that the central should use monetary

policy tools for controlling the boom and bust in housing prices. This will reduce the expected

bubbles in housing prices which will in turn reduce the risk in investments in housing market

(See, Cecchetti, et al; 2002; Mishkin, 2007). However, the literature shows that the central banks

monetary policy targeting housing market bubbles can only be effective if it focus on the drivers

bringing this up and downward trend in housing prices. But exposing the factors determining the

housing prices is also a controversial issue in the literature. Rajan (2009) showed that excessive

financial innovation resulted in the misallocation of capital to the housing sector. Whereas,

Caballero et al. (2008) and Warnock and Warnock (2009) mentioned that excessive saving in

financially weak countries led to the continuous capital inflows to the rich countries is the main

cause of housing prices bubbles in these developed world. However, other economists mentioned

that monetary policy transmission channels particularly low interest rates made the credit

cheaper and raised the demand for real state led to the higher prices in the housing market (Hume

and Sentance, 2009; Taylor, 2009).

1 Brett and Luciana (2010).2 The Economist 2005, Special Report, The global housing boom, pp. 52–54, June, 18.

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The case of the US also became as of special interest because of its unique upward trend in

housing prices recently. This development in the housing market has attracted a renewed interest

of the policy makers and researchers on the drivers of housing prices boom in US because of its

implications for the macroeconomic performance. Specifically, a number of studies have been

carried out to answer the question whether this upward swing in the housing prices is the result

of the fed looser monetary policy? However, when we study all the previous studies conducted

for the US or other economies, most of these studies focused on the impact of housing boom or

busts on macroeconomic conditions of US economy. Similarly, some studies analyzed this

housing prices boom issue in light of the Taylor rule. Despite of all these great interests in the

issue, no effort has been made in the literature to find out whether this boom in housing prices

resulted because of the fall in the nominal or real interest? Covering this gap in the literature we

will try to answer the following research questions.

1.1 Research Questions

Q1. Are housing prices respond to the fluctuations in the nominal or real interest rate?

Q2. Is there any direct relationship b/w the nominal interest rate and housing prices or this

influence comes indirectly through the real interest rate?

Q1. Is the interest rate affect the housing prices only in short run or also in long run?

1.2 Purpose of the Study

The main purpose of the present study is to examine the role of interest rate in US housing prices

determination.

2. Review of LiteratureThe present section deals with the reviews of the studies previously conducted on the issue of

interest rate as a tool of monetary policy and its effects on house prices for different economies

with different time periods.

Phiri (2016) investigates the asymmetric pass-through effects from monetary policy to housing

prices in South Africa by using time series data collected on monthly basis from January 1967 to

December 2015. The study uses the average real house price growth and government securities

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treasury bills as proxies for house price inflation and monetary policy instrument, respectively.

The study uses a momentum threshold autoregressive model and a corresponding threshold error

correction model (MTAR-TECM). The empirical results reveal a negative and significant

relationship between the prime interest rates and real house price inflation even though the

degree of pass-through is found to be quite low. In particular, the empirical results indicate that

an interest rate change of 1 percent will result in an opposite movement of house price inflation

of 0.02 percent.

Kuttner (2012) uses a cross-sectional regression model to evaluate the relationship between

monetary conditions and housing market. The compact form of the model he uses in the study is

of the following form;

Yi = β0 + β1rLi + β2rS

i + β3%∆MBi + β4Dieu + β5Di

em + ui

where; Y is the dependent variable which represents the real property price or the growth in

housing credit. The regressors include rS which represents the average real short-term interest

rate; rL which represents the average real lending rate and %∆MB which represents the

annualized average change in the real monetary base. Deu and Dem are the dummies used for euro-

area emerging market/transition economies. The results of the study suggest that the impact of

interest rates on house prices appears to be quiet modest while credit conditions play a larger role

in house price booms.

O’meara (2015) quantifies the extent of house price overvaluation in 10 Organization for

Economic Cooperation and Development (OECD) countries. The study specifies demand side

and supply side equations to estimate the equilibrium (fundamental) price of housing. This

approach is previously used by Muellbauer & Murphy (1997).The demand price for housing is

specified as:

Pft = αt + β1yt + β2popt – β3mortt – β4supplyt + β5rentt + ԑt

where (Pft) represents fundamental house prices which is a function of real disposable income per

capita (yt), the total population of the country (popt), the real (inflation adjusted) mortgage

interest rate (mortt), the supply on new dwellings (supplyt) and the real cost of renting a property

(rentt). The estimated supply equation is given by:

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Supplyt = αt + β1permitt – β2costt + β3rinvt + ut

Whereas, permitt is the number of permits issued for the construction of new housing units, cost t

refers to the total cost of residential construction and rinvt refers to the real residential

investment. Error correction models are estimated for the demand and supply equations and

actual and fundamental house prices are compared then. The empirical results of the study

suggest that for some countries deviations from the Taylor rule play a role in the sudden and

great increase in house prices and that a monetary policy stance less discretionary and more

closely aligned with the Taylor rule could curtail some of the imbalances in the housing market.

Guo and Wu (2013) empirically analyze the factors affecting the housing prices in Shanghai by

employing housing demand and supply approach. The results of the study show that lending

rates and Gross Domestic Product (GDP) are the factors affecting the house prices in Shanghai

more significantly while the other variables which include the total resident population, land

price index, per capita disposable income, consumer price index and the average residential

construction costs have much less influence in driving the house prices.

McCarthy & Peach (2004) analyzes the U.S. housing market to find the existence of a national

home price bubble. They present a model of the housing market in which demand price of

housing is determined by the housing stock, permanent income of households (proxied by

nondurables and services consumption) and the user cost for which the equation is given as

follows;

Pdt = α1ht + α2ct + α3ut

While the supply price is determined by residential investment and costs of construction and the

its equation is given as follows;

Ps = γ1 ( it - ht) + γccct

The findings of the study suggest that home prices rise in line with increases in personal income

and declines in nominal interest rates. Moreover, the study suggests that house prices are less

volatile than other asset prices, such as equity prices.

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Williams (2015) measures the effects of monetary policy on house prices by employing the

approach developed in Jorda, Schularick & Taylor (2015a,b) to a sample of 17 countries for the

period 1870-2013 (except for the interwar period 1914-45 and the oil crisis years of 1973-80).

The findings of the study suggest that monetary policy actions have sizeable and significant

effects on house prices in advanced economies.

Del Negro & Otrok (2007) estimate a six-variable VAR on U.S. data spanning 1986 through

2005. The variables included in the system were the house price, total reserves, Consumer Price

Index (CPI) inflation, Gross Domestic Product (GDP) growth, the 30-year mortgage rate and the

Federal funds rate. Monetary shocks were identified using sign restrictions. The main finding of

the study is that a 25 basis point expansionary monetary policy shock leads to a statistically

significant 0.9% appreciation immediately and reducing to only 0.2% after ten quarters.

Goodhart & Hofmann (2008) use a panel VAR to examine the relationship between house prices,

macroeconomic variables and other financial indicators in 17 industrialized countries. The six

variables in their model were real Gross Domestic Product (GDP) growth, Consumer Price Index

(CPI) inflation, the short-term nominal interest rate, house price growth, broad money growth

and nominal private credit growth. The results show Granger-causal relationships between many

of the variables and in particular a causal relationship from interest rates to house prices and

credit growth. The findings of the study reveal that 25 basis points orthogonalized expansionary

interest rate innovation leads to statistically significant 0.8% increase in house prices. They add

that there is no immediate impact seen rather the effect builds slowly, reaching 0.4% after 10

quarters and gradually achieving its maximum after 40 quarters. These findings are in contrast to

those in Del Negro & Otrok (2007) where their findings say that 0.9% peak occurs immediately

and vanishes rapidly.

Jarocinski & Smets (2008) presented two sets of estimates from Bayesian VARs for the U.S.: on

in levels and an alternative first-difference specification. Their nine-variable models include

output, consumption, the Gross Domestic Product (GDP) deflator, housing investment, the house

price, the short-term interest rate, the term spread, a commodity price index and the money

supply. They identify structural shocks via sign restrictions on the impulse response functions as

in Del Negro & Otrok (2007). In the levels VAR, an expansionary 25 basis point monetary

policy shock leads to gradual rise in house prices, peaking at a statistically significant 0.5% after

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10 quarters which is accompanied by a decline in the long-term interest rate of roughly 10 basis

points. The effects subsequently diminish and 20 quarters after the shock the house price has

returned to its mean. The differenced VAR yielded somewhat larger and more persistent

estimates, but the confidence intervals are much wider, especially at longer horizons.

Sá et al. (2011) reports panel VAR results for 18 Organization for Economic Cooperation and

Development (OECD) countries from a 12-variable model, using data from 1984 through 2006.

In addition to the standard macro variables (output, the price level, consumption, non-residential

and residential investment, short- and long-term interest rates and a measure of credit), the

specification also include four variables reflecting global factors: world Gross Domestic Product

(GDP), world prices, the trade-weighted exchange rate and the current account balance. The

shocks are identified using sign restrictions just like in the studies of Del Negro & Otrok (2007)

and Jarocinski & Smets (2008). The results are in accordance with those of Jarocinski & Smets

(2008): the response to a 25 basis expansionary shock is initially slightly negative, subsequently

rising to a statistically significant but modest 0.3% effect after 10 quarters. Over a similar

horizon, the long-term interest rate declines by approximately 10 basis points. The response is

larger for countries with more sophisticated financial systems, where the response at ten quarters

is closer to 0.5%. For all countries, the effect subsequently diminishes, falling to 0.1% after 30

quarters.

Gupta and Kabundi (2009) examine the impact of monetary policy on house price inflation for

the nine census divisions. They use a factor-augmented VAR with 126 variables and find that in

general a monetary policy shock has a negative impact on house price inflation, though

heterogeneous across the nine divisions.

Gupta et al. (2010) examine the impact of monetary policy shocks on the U.S. housing sector

using a large-scale Bayesian VAR model with 143 macroeconomic variables, including 21

housing variables related to house prices and housing investment, on both the national level and

the four census regions. In order to test whether the financial market liberalization of the early

1980s has affected such impact, they split their sample in the period prior to 1982 and after 1989,

to find that the negative effect of a monetary policy shock on real house prices is larger in the

post-liberalization period at both the national and the regional level, though heterogeneous across

regions.

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Christidou & Konstantinou (2011) investigate the effects of monetary policy on housing markets

of 48 U.S. states. They employ quarterly data covering the period from the first quarter of 1988

to the fourth quarter of 2009. The variables include the Federal Funds rate and a commodity

price index, real personal income, the GDP deflator, housing investment, and real house prices.

Vector Autoregressive (VAR) model is used and the findings suggest that the effects of a

contractionary monetary policy on real housing prices and housing investment are heterogeneous

across the states both in magnitude and duration.

3. Data and Methodology 3.1 Data and Definition of the Variables of the Study

The main objective of the present study is to investigate the role of the interest rate in the

determination of US housing prices. For this purpose time series monthly data over the period

January-2000 to December-2015 has been used in the present study. The data has been collected

from various sources including US Federal Housing and Finance Agency, US Bureau of Labor

Statistics, US Bureau of Economic Analysis and Federal Reserve annual reports. The detail of

all the variables used in the present study has been given in table 3.1.

Table 3.1: Definition of Variables of the Study

Variables Definition Symbols

Housing Price US housing Price index HPC

Gross Domestic Product US Gross Domestic Product in trillions of US dollar. GDPC

Population Growth Rate US monthly population growth rate. POPGC

Nominal Interest Rate US nominal interest rate INTC

Inflation US monthly inflation rate INFC

Real Interest Rate US real interest rate which is computed by taking the difference between the nominal interest rate and inflation rate.

RINTC

In table 3.1 information about all the variables of the study has been given. The superscript “c”

on the symbols of all variables show that all these variables has been used in deviation form. For

converting the data into deviation form and extracting only the cyclical component Hodrick

Prescott(HP) filter method is used. The purpose of the application of the HP filter method to data

is to remove trend from data and making it stationary. Eviews software is used for the

computation of the results.

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3.2 Model Specification

The study will follow the methodological approach developed by Mingzhen Guo and Qing Wu

(2013). The model of the study is based on the law of demand and supply as devised by the

economic theory. Housing units are transacted in the market just like the other commodities or

goods. The price of a housing unit is determined by the forces of demand and supply. The impact

of factors affecting the price directly affects supply and demand which then affects the price of

housing units. So demand and supply can be considered as the middle of the bridge in this case.

Therefore, the factors can be grouped into two separate groups i.e. factors affecting demand and

factors affecting supply of the housing prices. In other words, we can say that housing prices are

the function of housing units demand and supply.

We can setup the housing prices supply demand equation as follows;

HPrice = f (DHousing, SHousing) (3.1)

Where; HPrice represents the housing price index, DHousing represents demand for a housing unit and

SHousing represents the supply of housing units.

3.2.1Demand Equation

Firstly, the price of a housing unit is a very crucial factor to determine housing demand. If prices

are too high, the effective demand will decrease while effective demand will increase if the

prices are considerably low. Secondly, the resident population has a great influence over the

demand for housing. As the resident population increases, the demand for housing will inevitably

increase. Even if the relationship is not linear, housing demand will be affected by the increase in

population. Thirdly, the per capita annual disposable income is a direct factor in the formation of

effective demand. As long as there is a strong potential demand for housing, and there are

enough disposable income cases, the capacity to pay will turn into effective demand. Fourthly,

interest rate has a great impact on buying a house of the buyers. High interest rates and capital

costs will suppress the corresponding desire to buy a house. Instead, low loan interest rates and

low cost of capital will increase the desire to buy a house. Fifthly, Consumer Price Index

(CPI)/inflation devalue value for money. Capital owners will fund for investment to expect

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capital preservation and appreciation, and investing commodity housing is a good choice. Based

on the above mentioned factors, now we build housing demand function, as follows:

DHousing = f (HPrice, POP, GDP, INT, INF) (3.2)

Whereas,

HPrice = Housing Price

POP = Total Population/Population Growth Rate

GDP = Gross Domestic Product

INT = Interest Rate

INF = Inflation Rate

3.2.2Supply EquationFirstly, when housing prices are high, developers will increase supply while there will be a

reduction in the supply if housing prices are low. Secondly, benchmark lending rate or interest

rate is directly related to the cost of the developer’s use of loan funds. When the interest rate /

lending rate is high, the corresponding cost is also high. So that investment in housing

construction decreases and developer reduces the quantity. On the contrary, when interest rate /

lending rate is low, the corresponding cost will be low and the investment in housing

construction will increase which means greater supply of housing units by the developer.

Thirdly, the price of land constitutes a large part of housing construction costs. With high land

costs, developments costs are high, in the case of limited development funds; the supply of

housing is reduced. Fourthly, when the cost of housing construction is high, the developer

decrease the housing supply and increases if the cost of housing construction is low. Fifthly, as

the Gross Domestic Product (GDP) increases, the reflection on infrastructure and real estate

development increases with a corresponding increase in supply.

Based on the above factors, the supply of housing construction is given as follows;

SHousing = f (HPrice, GDP, INT) (3.3)

Whereas,

HPrice = Housing Price

GDP = Gross Domestic Product

INT = Interest Rate

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3.2.3Housing Price Model

Economic theory shows that factors affecting the price directly affect demand and supply and

then affects the price, so demand and supply is the middle of the bridge. Therefore, these factors

can be grouped into the factors affecting demand and affecting supply. Hence, from equation

(3.1) we knew that we can write housing prices is the function of housing demand and supply.

HPrice = F (DHousing, SHousing)

Now for obtaining the housing price equation we put equation (3.2) and equation (3.3) into equation (3.1).

Hence, we get the following general form of the housing price model.

HPrice = f (HPrice, POP, GDP, INT, INF) (3.4)

Now because the specific purpose of the current study is to examine whether US housing price is affecting by the nominal interest or the real interest. Hence, for the empirical estimation we have used the following two modified versions of the housing price model into equation form.

3.2.3.1 Housing Price Model with Nominal Interest Rate

HPC = 𝛼0 + 𝛼1INTC + 𝛼2INFC + 𝛼3POPGC + 𝛼4GDPC + ε1 (3.5)

Equation (3.5) shows the Housing price model with nominal interest rate. Here in this equation we have included nominal interest rate. However, we have excluded the real interest rate from the model. In the model HPC has been used as dependent variable. Whereas, POPGC, GDPC, INTC and INFC has been used as independent variables. Moreover, 𝛼0, 𝛼1, 𝛼2, 𝛼3 and 𝛼4 are the relevant coefficients and ε1 is the error term.

3.2.3.2 Housing Price Model with Real Interest Rate

Now for examining the role of the real interest rate in the US housing prices, we have excluded nominal interest rate and inflation rate from equation (3.5) and included the real interest rate. This new model is showing by equation (3.6).

HPC = 𝛽0 + 𝛽1RINTC + 𝛽3POPGC + 𝛽4GDPC + ε2 (3.6)

Equation (3.6) shows the housing price model with real interest rate. In the model HPC has been used as dependent variable. Whereas, POPGC, GDPC and RINTC has been used as independent variables. Moreover, 𝛽0, 𝛽1, 𝛽2 and 𝛽3 are the relevant coefficients and ε2 is the error term.

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4. Results and Discussions

The results of the study is divided into two main parts i.e. section 4.1 and section 4.2. First, in

section 4.1 results for the housing price model with nominal interest rate has been computed.

For showing the role of the nominal interest rate in the US housing price different econometric

techniques including Ordinary Least Squares, Granger Causality Test and Vector Auto

Regression has been applied. Similarly, section 4.2 shows the results of our housing price model

with real interest rate. For examining the role of the real interest rate in the US housing price

Ordinary Least Squares, Granger Causality Test and Vector Auto Regression has been used.

4.1. Results of Housing Price Model with Nominal Interest Rate

For investigating the role of the nominal interest rate in the US housing price Ordinary Least

Squares method has been applied. The regression results computed has been given in section

4.1.1.

4.1.1. Regression Results of the Housing Price Model with Nominal Interest Rate

Different versions of our housing price model have been used for computing the regression

results. The results are given in table 4.1 as follows.

Table.4.1: Regression Results of Housing Price Moel with Nominal Interest Rate

Dependent Variable: HPC

Method: Ordinary Least SquaresNewey-West HAC Standard Errors and Covariance

HPC = 𝛼0 + 𝛼1INTC + 𝛼2INFC + 𝛼3POPGC + 𝛼4GDPC + ε1

Regression-1 Regression-2 Regression-3 Regression-4Independent Variables

Coefficient(St. Error)

Independent Variables






INTC -5.875147**(0.444820)

INTC 5.900961***(1.082213)

INTC 6.051620**(1.076960)

INTC -6.214616**(1.069181)

Intercept -3.98E-13(0.251416)

INFC 1.361506(0.862475)

INFC 1.528996*(0.827901)

INFC 1.544844*(0.832905)

--- --- Intercept -3.63E-13(0.513211)

POPGC 3.629614**(1.435267)

POPGC 3.747277**(1.448698)

--- --- --- --- Intercept -3.28E-13(0.496511)

GDPC -1.370514(2.34339)

--- --- --- --- --- --- Intercept 3.54E-12(0.495555)

R-Square: 0.48Adj. R-Square:0.47DW Statistic:0.41




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Asterisks “*” , “**” , “***” stands for 90%, 95%, and 99% confidence level Figures in parenthesis show SEs(Standard Errors) of the estimates. DW stands for Durbin Watson Statistic

In table 4.1 we have estimated four regression results i.e. Regression-1, Regression-2,

Regression-3 and Regression-4. The main purpose behind the computation of the regression

results for the various versions of our housing price model is to examine the importance of all of

our explanatory variables in our housing price model.

First, in regression-1 we have included only the nominal interest rate as explanatory variable in

our housing price model. All other explanatory variables has been excluded from the model.

Regression-1 results shows that the coefficient of the nominal interest rate turned positively

significant at 5% level of significance. However, the sign of the nominal interest rate is positive

which is against our expectations. One reason for this can be the exclusion of other important

variables from the model. Moreover, R-square and Durbin Watson statistic values are also very

low showing the significance of the missing variables in our model. Similarly, Regression-2

results has been computed by including inflation rate in the model. The results show that

nominal interest rate is positively significant. However, inflation remained insignificant. The R-

square value is 0.49 which shows that overall the fit is not good. Similarly, the Durbin Watson

statistic value is 0.81 showing the autocorrelation problem in the model. After that Regression-3

has been computed by including population growth rate as additional explanatory variable in the

model. The results show that inflation and population growth rate turned positively significant.

However, turned significant but with a positive unexpected sign. Here, the R-square value is 0.51

and the Durbin Watson statistic value is 1.23. Finally, we have included another explanatory

variable real Gross Domestic Product in the model and computed regression results which is

showing by regression-4 in the table. The results show that all the explanatory variables i.e.

nominal interest rate, inflation rate and population growth rate turned positively significant.

However, the real Gross Domestic Product turned insignificant. In the model the R-square value

is 0.57 showing that 57% variation in the dependent variable is explaining by all the independent

variables. Furthermore, the Durbin Watson statistic value is 1.67 showing that the reliability of

the results.

4.1.2. Granger Causality Results of the Housing Price Model with Nominal Interest Rate

In time series analysis the Granger causality test is widely used to know whether in the time

series data one variable can forecast the other time series variable. The model is first proposed in

15

1969 by Granger (1969). Ordinarily, regressions reflect "mere" correlations, but Clive Granger

argued that causality in economics could be reflected by measuring the ability of predicting the

future values of a time series using past values of another time series. Since the question of "real

causality" is deeply philosophical, econometricians assert that the Granger test finds only

"predictive causality" (Diebold, 2001).

The Granger Causality test results are given in table 4.2 as follows.

Table 4.2: Results of Granger Causality Test

Null Hypothesis Obs F-Statistic Prob. INFC does not Granger Cause HPC

HPC Does not Granger Cause INFC 190

0.61553 0.58341

0.54150.5590

INTC does not Granger Cause HPC

HPC does not Granger Cause INTC

190

7.30340 0.1805

0.01091.72818

POPGC does not Granger Cause HPC

HPC does not Granger Cause POPGC190 0.38645

0.107600.68000.8980

GDPC does not Granger Cause HPC

HPC does not Granger Cause GDPC190 0.31739

1.619160.72840.2009

INTC does not Granger Cause INFC

INFC does not Granger Cause INTC190 0.32809

3.647960.72070.0279

POPGC does not Granger Cause INFC

INFC does not Granger Cause POPGC 190 1.38474

0.005420.25300.9946

GDPC does not Granger Cause INFC

INFC does not Granger Cause GDPC190 3.47330

1.051290.03310.3516

POPGC does not Granger Cause INTC

INTC does not Granger Cause POPGC190 1.67976

2.042840.18920.1326

GDPC does not Granger Cause INTC

INTC does not Granger Cause GDPC190 1.53056

2.458330.21910.0884

GDPC does not Granger Cause POPGC

POPGC does not Granger Cause GDPC190 0.33342

0.408080.71690.6655

The Granger Causality test results show that nominal interest rate shows a uni-directional

relationship with the housing price index. Similarly, inflation with nominal interest rate, Gross

Domestic Product with inflation rate and nominal interest rate with Gross Domestic Product also

show a uni-directional relationship. All other hypothesis for other variables cannot be rejected

because the P-value turned insignificant for these variables.

4.1.3. VAR Results of the Housing Price Model with Nominal Interest Rate

Table 4.3 to table 4.8 shows the VAR results. First we have checked the individual significance

of the variables for each model of the whole system equation. Then, we have computed the Joint

16

significance of all the variables of different models by using the Wald test. Finally the Cholesky

decomposition test is used for the computation of impulse response functions.

4.3.1.1: Individual Significance of the Variables of VAR Model

The individual significance of the explanatory variables has been checked for all the five models

of the system i.e. Housing Price Model, Population Growth Model, Gross Domestic Model,

Inflation Model and Interest Rate Model.

First, the Housing Price model shows that four variables lag variables of housing price index

and nominal interest rate turned significant in the model. Whereas, other variables in the model

remained insignificant. Similarly, in the population growth model lag values of the population

growth rate, inflation rate and interest rate turned significant. Moreover, in the Gross Domestic

Product model lag variables of the Gross Domestic Product remained significant. Whereas, all

other variables turned insignificant. Furthermore, in the inflation rate model lag values of the

population growth rate, Gross Domestic Product and inflation rate became significant. Finally in

the interest rate model lag variables of the housing price index, population growth rate, inflation

rate and nominal interest rate turned significant.

Overall, the nominal housing price index model shows the role of the nominal interest in the

determination of the US housing prices.

Table 4.3:VAR Test Results for Housing Model with Nominal Interest RateVector Autoregression EstimatesSample (adjusted): 2000M03 2015M12Included observations: 190 after adjustmentsStandard errors in ( ) & t-statistics in [ ]

HPC POPGC GDPC INFC INTC

HPC(-1) 1.815750** (0.03919)[ 46.3272]

0.007636 (0.01484)[ 0.51470]

0.001798 (0.01429)[ 0.12585]

0.020226 (0.03364)[ 0.60135]

0.026413** (0.01520)[ 1.73748]

HPC(-2) -0.845068** (0.03998)[-21.1391]

-0.015964 (0.01513)[-1.05496]

0.000333 (0.01458)[ 0.02284]

-0.003657 (0.03431)[-0.10659]

-0.016409 (0.01551)[-1.05828]

POPGC(-1) 0.135253 (0.19871)[ 0.68067]

0.572605** (0.07522)[ 7.61270]

-0.040375 (0.07245)[-0.55728]

0.040930 (0.17052)[ 0.24003]

0.113374 (0.07707)[ 1.47104]

POPGC(-2) -0.166953 (0.19182)[-0.87038]

-0.133846** (0.07261)[-1.84337]

0.051877 (0.06994)[ 0.74176]

0.273881** (0.16461)[ 1.66380]

-0.133544** (0.07440)[-1.79496]

GDPC(-1) -0.135818 (0.37660)[-0.36065]

-0.088875 (0.14255)[-0.62344]

0.390582** (0.13731)[ 2.84456]

0.791451** (0.32318)[ 2.44893]

0.243310** (0.14607)[ 1.66573]

17

GDPC(-2) -0.282848 (0.38651)[-0.73181]

0.001085 (0.14631)[ 0.00742]

0.209765 (0.14092)[ 1.48852]

-0.587957** (0.33169)[-1.77262]

-0.098944 (0.14991)[-0.66001]

INFC(-1) 0.072732 (0.08299)[ 0.87643]

0.019554 (0.03141)[ 0.62246]

-0.010340 (0.03026)[-0.34173]

0.569860** (0.07122)[ 8.00179]

0.062022** (0.03219)[ 1.92687]

INFC(-2) -0.105978 (0.08253)[-1.28411]

0.005508** (0.03124)[ 0.17632]

0.032042 (0.03009)[ 1.06483]

-0.290293** (0.07083)[-4.09873]

-0.005054 (0.03201)[-0.15789]

INTC(-1) 0.393315** (0.17697)[ 2.22243]

0.025020** (0.06699)[ 0.37348]

0.072252 (0.06453)[ 1.11974]

-0.126168 (0.15187)[-0.83074]

1.276017** (0.06864)[ 18.5893]

INTC(-2) -0.286625** (0.17741)[-1.61557]

0.078755** (0.06716)[ 1.17269]

-0.048045 (0.06469)[-0.74275]

-0.042201 (0.15225)[-0.27718]

-0.421019** (0.06881)[-6.11833]

C -0.005016 (0.02784)[-0.18013]

0.000303 (0.01054)[ 0.02875]

0.004652 (0.01015)[ 0.45826]

0.000794 (0.02389)[ 0.03323]

0.004421 (0.01080)[ 0.40933]

R-squaredAdj. R-squaredSum sq. residsS.E. equationF-statistic

0.8940910.883761

26.13434

0.382102

3011.281

0.6722930.667225

3.744766

0.144639

10.61648

0.5840020.574002

3.474219

0.139316

7.100080

0.6445600.637943

19.24674

0.327908

9.409892

0.9336600.929954

3.931642

0.148204

251.9235 ** shows 5% level of significance

4.3.1.2: Joint l Significance of the Variables of VAR Model

The joint significance of the variables has been checked by using the Wald test.

The individual significance of the explanatory variables has been checked for all the five models

of the system i.e. Housing Price Model, Population Growth Model, Gross Domestic Model,

Inflation Model and Interest Rate Model. The overall significance of all the five models i.e.

housing price index, population growth rate, Gross Domestic Product, inflation rate and nominal

interest rate has been estimated. The results computed are given in tables 4.4, 4.5, 4.6, 4.7 and

4.8 respectively. By applying the Wald test for showing the joint significance of the explanatory

variables in all the five models we have put zero restriction on the explanatory variables with the

hypothesis that these all these explanatory variables doesn’t influence the dependent variables.

However, all of these hypotheses have been rejected and it is confirmed that all the explanatory

variables in all the models does influence the explained variables.

From the housing price index model it is clear that nominal interest rate does play role in the

determination of the US housing price rise.

Table 4.4 : HPC = C(1)* HPC(-1) + C(2)* HPC(-2) + C(3)*POPGC(-1) + C(4)*POPGC(-2) + C(5)*GDPC(-1) +

C(6)*GDPC(-2) + C(7)*INFC(-1) + C(8)*INFC(-2) + C(9)*INTC(-1) + C(10)*INTC(-2) + C(11)

18

Null Hypothesis: C(1)=C(2)=C(3)=C(4)=C(5)=C(6)=C(7)=C(8)=C(9)=(10)=C(11)=0Test Statistic Value Df ProbabilityChi-Square 30112.84 11 0.0000

Normalized Restriction (= 0) Value Std. Err.C(1) 1.815750 0.039194C(2) -0.845068 0.039976C(3) 0.393315 0.176975C(4) -0.286625 0.177414C(5) 0.072732 0.082987C(6) -0.105978 0.082530C(7) 0.135253 0.198706C(8) -0.166953 0.191817C(9) -0.135818 0.376595C(10) -0.282848 0.386506C(11) -0.005016 0.027844

Restrictions are linear in coefficients

Table 4.5: POPGC = C(12)*HPC(-1) + C(13)*HPC(-2) + C(14)*POPGC(-1) + C(15)*POPGC(-2) + C(16)*GDPC(-1) + C(17)*GDPC(-2) + C(18)*INFC(-1) + C(19)*INFC(-2) + C(20)*INTC(-1) + C(21)*INTC(-2) + C(22)

Null Hypothesis: C(12)=C(13)=C(14)=C(15)=C(16)=C(17)=C(18)=C(19)=C(20)=C(21)=C(22)=0Test Statistic Value Df ProbabilityChi-Square 2520.191 11 0.0000

Normalized Restriction (= 0) Value Std. Err.C(12) 0.026413 0.015202C(13) -0.016409 0.015505C(14) 1.276017 0.068642C(15) -0.421019 0.068813C(16) 0.062022 0.032188C(17) -0.005054 0.032011C(18) 0.113374 0.077071C(19) -0.133544 0.074399C(20) 0.243310 0.146068C(21) -0.098944 0.149912C(22) 0.004421 0.010800


Table 4.6: GDPC = C(23)* HPC(-1) + C(24)* HPC(-2) + C(25)*POPGC(-1) + C(26)*POPGC(-2) + C(27)*GDPC(-1) + C(28)*GDPC(-2) + C(29)*INFC(-1) + C(30)*INFC(-2) + C(31)*INTC(-1) + C(32)*INTC(-2) + C(33)


Normalized Restriction (= 0) Value Std. Err.C(23) 0.020226 0.033635C(24) -0.003657 0.034307C(25) -0.126168 0.151874C(26) -0.042201 0.152251C(27) 0.569860 0.071217

19

C(28) -0.290293 0.070825C(29) 0.040930 0.170523C(30) 0.273881 0.164611C(31) 0.791451 0.323182C(32) -0.587957 0.331687C(33) 0.000794 0.023895


Table 4.7: INFC = C(34)* HPC(-1) + C(35)* HPC(-2) + C(36)*POPGC(-1) + C(37)*POPGC(-2) + C(38)*GDPC(-1) + C(39)*GDPC(-2) + C(40)*INFC(-1) + C(41)*INFC(-2) + C(42)*INTC(-1) + C(43)*INTC(-2) + C(44)


Normalized Restriction (= 0) Value Std. Err.C(34) 0.007636 0.014836C(35) -0.015964 0.015133C(36) 0.025020 0.066991C(37) 0.078755 0.067158C(38) 0.019554 0.031413C(39) 0.005508 0.031241C(40) 0.572605 0.075217C(41) -0.133846 0.072610C(42) -0.088875 0.142555C(43) 0.001085 0.146306C(44) 0.000303 0.010540


Table 4.8: INTC = C(45)* HPC(-1) + C(46)* HPC(-2) + C(47)*POPGC(-1) + C(48)*POPGC(-2) + C(49)*GDPC(-1) + C(50)*GDPC(-2) + C(51)*INFC(-1) + C(52)*INFC(-2) + C(53)*INTC(-1) + C(54)*INTC(-2) + C(55)


Normalized Restriction (= 0) Value Std. Err.C(45) 0.001798 0.014290C(46) 0.000333 0.014576C(47) 0.072252 0.064526C(48) -0.048045 0.064686C(49) -0.010340 0.030257C(50) 0.032042 0.030091C(51) -0.040375 0.072449C(52) 0.051877 0.069937C(53) 0.390582 0.137309C(54) 0.209765 0.140922C(55) 0.004652 0.010152


4.3.1.3: Impulse Response Function Results of VAR Model

20

Cholesky decomposition is used for examining the response of the dependent variables to

independent variables in all the five models. The results are given in figure 4.1 as follows.

Figure. 4.1: Impulse Response Function Results

The results shows that housing price index in the first model is affected by affected by the shocks

from all the explanatory variables i.e. lag housing price index, population growth rate, Gross

Domestic Product, inflation rate and nominal interest rate.

Similarly, the models of population growth rate, Gross Domestic Product, inflation rate and

nominal interest rate also shows the affect of the shocks on the dependent variables from the

independent variables.

21

4.2. Results of Housing Price Model with Real Interest Rate

For investigating the role of the real interest rate in the US housing price Ordinary Least Squares

method has been applied. The regression results computed has been given in section 4.9.

4.2.1. Regression Results of the Housing Price Model with Real Interest Rate

Different versions of our housing price model have been used for computing the regression

results. The results are given in table 4.9 as follows.

Table.4.9: Regression Results of Housing Price Model with Real Interest Rate

Dependent Variable: HPC

Method: Ordinary Least SquaresNewey-West HAC Standard Errors and CovarianceHPC = 𝛽0 + 𝛽1RINTC + 𝛽3POPGC + 𝛽4GDPC + ε2

Regression-1 Regression-2 Regression-3Independent Variables






RINTC -3.514012**(0.877591)

RINTC 3.532757**(0.431006)

RINTC -3.302929**(0.454986)

Intercept -4.93E-12(0.614480)

POPGC -1.476015(1.698600)

POPGC -1.327662(1.695377)

Intercept -4.97E-12(0.299749)

GDPC 3.041245(1.987374)

Intercept 2.42E-12(0.298691)

R-Square:0.25Adj. R-Square:0.24DW Statistic:0.23



Asterisks “*” , “**” , “***” stands for 90%, 95%, and 99% confidence level Figures in parenthesis show SEs(Standard Errors) of the estimates. DW stands for Durbin Watson Statistic

In table 4.9 we have estimated three regression results i.e. Regression-1, Regression-2 and

Regression-3. The main purpose behind the computation of the regression results for the various

versions of our housing price model is to examine the importance of all of our explanatory

variables in our housing price model.

22

First, in regression-1 we have included only the interest rate as explanatory variable in our

housing price model. All other explanatory variables has been excluded from the model.

Regression-1 results shows that the coefficient of the real interest rate turned negatively

significant at 5% level of significance. Moreover, R-square value is 0.25 and the Durbin Watson

statistic value is 0.23 showing the significance of the missing variables in our model. Similarly,

Regression-2 results has been computed by including population growth rate in the model. The

results show that real interest rate is significant but with unexpected positive sign. However,

population growth rate and intercept remained insignificant. The R-square value is 0.36 which

shows that overall the fit is not good. Similarly, the Durbin Watson statistic value is 0.97

showing the autocorrelation problem in the model. Finally, Regression-3 has been computed by

including Gross Domestic Product as additional explanatory variable in the model. The results

show that real interest rate turned significant with the expected negative sign. However, all the

coefficients of all other variables i.e. population growth rate, Gross Domestic Product and

intercept remained insignificant. Here, the R-square value is 0.49 and the Durbin Watson statistic

value is 1.37.

4.2.2. Granger Causality Results of the Housing Price Model with Real Interest Rate

The Granger Causality test results are given in table 4.10 as follows.

Table 4.10: Results of Granger Causality Test

Null Hypothesis Obs F-Statistic Prob. POPGC does not Granger Cause HPC

HPC does not Granger Cause POPGC190 0.38645

0.107600.68000.8980

GDPC does not Granger Cause HPC

HPC does not Granger Cause GDPC190 0.31739

1.619160.72840.2009

RINTC does not Granger Cause HPC

HPC does not Granger Cause RINTC 190 0.20011

7.143610.81880.0010

GDPC does not Granger Cause POPGC

POPGC does not Granger Cause GDPC190 0.33342

0.408080.71690.6655

RINTC does not Granger Cause POPGC

POPGC does not Granger Cause RINTC190 1.43957

1.252340.23970.2882

RINTC does not Granger Cause GDPC

GDPC does not Granger Cause RINTC190 1.34522

5.028100.26300.0075

The Granger Causality test results show that two variables i.e. housing price index and Gross

Domestic Product showed a uni-directional relationship with the real interest rate. However,

23

other hypothesis for all other variables cannot be rejected because the P-value turned

insignificant for these variables.

4.2.3. VAR Results of the Housing Price Model with Real Interest Rate

Table 4.11 to table 4.15 shows the VAR results. First we have checked the individual

significance of the variables for each model of the whole system equation. Then, we have

computed the Joint significance of all the variables of different models by using the Wald test.

Finally the Cholesky decomposition test is used for the computation of impulse response

functions.

4.3.2.1: Individual Significance of the Variables of VAR Model

The individual significance of the explanatory variables has been checked for all the four models

of the system i.e. Housing Price Model, Population Growth Model, Gross Domestic Model and

Real Interest Rate.

First, the Housing Price model shows that two lag variables of housing price index turned

significant in the model. Whereas, other variables in the model remained insignificant.

Similarly, in the population growth rate model and Gross Domestic Product models only lag

values of the population growth rate and Gross Domestic Product turned significant. Finally, in

the real interest rate model lag values of the Gross Domestic Product and real interest rate

remained significant whereas, all other variables turned insgificant.

Table 4.11:VAR Test Results for Housing Model with Real Interest RateVector Autoregression EstimatesSample (adjusted): 2000M03 2015M12Included observations: 190 after adjustmentsStandard errors in ( ) & t-statistics in [ ]

HPC POPGC GDPC RINTC

HPC(-1) 1.837243** (0.03859)[ 47.6106]

0.009268(0.01462)[ 0.63376]

0.004528 (0.01390)[ 0.32585]

0.045137 (0.04012)[ 1.12512]

HPC(-2) -0.861653** (0.03949)[-21.8208]

-0.012991(0.01497)[-0.86809]

-0.000517(0.01422)[-0.03637]

-0.019883(0.04105)[-0.48434]

POPGC(-1) 0.111768 (0.19510)[ 0.57286]

0.612798** (0.07394)[ 8.28770]

-0.030900 (0.07026)[-0.43981]

0.257252 (0.20283)[ 1.26831]

POPGC(-2) -0.165344 (0.19241)[-0.85932]

-0.112004 (0.07292)[-1.53598]

0.058527 (0.06929)[ 0.84471]

-0.286856(0.20003)[-1.43405]

24

GDPC(-1) -0.052506 (0.37752)[-0.13908]

-0.049417 (0.14307)[-0.34541]

0.410995** (0.13594)[ 3.02331]

-0.217697 (0.39247)[-0.55469]

GDPC(-2) -0.231438 (0.38277)[-0.60463]

0.074694(0.14506)[ 0.51491]

0.236981**(0.13784)[ 1.71930]

0.959752** (0.39793)[ 2.41184]

RINTC(-1) 0.006410(0.06901)[ 0.09289]

0.019803 (0.02615)[ 0.75719]

0.030287 (0.02485)[ 1.21882]

0.831894**(0.07174)[ 11.5957]

RINTC(-2) 0.035127 (0.06842)[ 0.51339]

0.021652(0.02593)[ 0.83501]

-0.031382(0.02464) [-1.27373]

-0.237376** (0.07113)[-3.33718]

C -0.001693(0.02810)[-0.06024]

0.001089 (0.01065)[ 0.10222]

0.005233(0.01012)[ 0.51704]

0.012537 (0.02922)[ 0.42911]

R-squared Adj. R-squared Sum sq. resids S.E. equation F-statistic

0.813896 0.803626 26.99660 0.386203 3683.868

0.350057 0.321330

3.877419 0.146363 12.18576

0.278556 0.246669 3.500646

0.139070

8.735713

0.677937 0.663702 29.17748

0.401499

47.62525 ** shows 5% level of significance

4.3.2.2: Joint l Significance of the Variables of VAR Model

The joint significance of the variables has been checked by using the Wald test.

The significance of the explanatory variables has been checked for all the five models of the

system i.e. Housing Price Model, Population Growth Model, Gross Domestic Model and Real

Interest Rate Model. The overall significance of all the four models i.e. housing price index,

population growth rate, Gross Domestic Product real interest has been estimated. The results

computed are given in tables 4.12, 4.13, 4.14 and 4.15 respectively. By applying the Wald test

for showing the joint significance of the explanatory variables in all the four models we have put

zero restriction on the explanatory variables with the hypothesis that these all these explanatory

variables doesn’t influence the dependent variables. However, all of these hypotheses have been

rejected and it is confirmed that all the explanatory variables in all the models does influence the

explained variables.

Table. 4.12: HPC = C(1)* HPC(-1) + C(2)* HPC(-2) + C(3)*POPGC(-1) + C(4)*POPGC(-2) + C(5)*GDPC(-1) + C(6)*GDPC(-2) + C(7)*RINTC(-1) + C(8)*RINTC(-2) + C(9)

Null Hypothesis: C(1)=C(2)=C(3)=C(4)=C(5)=C(6)=C(7)=C(8)=C(9)=0Test Statistic Value Df ProbabilityChi-Square 29470.97 9 0.0000

Normalized Restriction (= 0) Value Std. Err.C(1) 1.837243 0.038589C(2) -0.861653 0.039488

25

C(3) 0.111768 0.195104C(4) -0.165344 0.192411C(5) -0.052506 0.377515C(6) -0.231438 0.382774C(7) 0.006410 0.069008C(8) 0.035127 0.068421C(9) -0.001693 0.028104


Table. 4.13: POPGC = C(10)* HPC(-1) + C(11)* HPC (-2) + C(12)*POPGC(-1) + C(13)*POPGC(-2) + C(14)*GDPC(-1) + C(15)*GDPC(-2) + C(16)*RINTC(-1) + C(17)*RINTC(-2) + C(18)


Normalized Restriction (= 0) Value Std. Err.C(10) 0.009268 0.014624C(11) -0.012991 0.014965C(12) 0.612798 0.073941C(13) -0.112004 0.072920C(14) -0.049417 0.143071C(15) 0.074694 0.145064C(16) 0.019803 0.026153C(17) 0.021652 0.025930C(18) 0.001089 0.010651


Table. 4.14: GDPC = C(19)* HPC(-1) + C(20)* HPC(-2) + C(21)*POPGC(-1) + C(22)*POPGC(-2) + C(23)*GDPC(-1) + C(24)*GDPC(-2) + C(25)*RINTC(-1) + C(26)*RINTC(-2) + C(27)


Normalized Restriction (= 0) Value Std. Err.C(19) 0.004528 0.013896C(20) -0.000517 0.014219C(21) -0.030900 0.070256C(22) 0.058527 0.069287C(23) 0.410995 0.135942C(624 0.236981 0.137836C(25) 0.030287 0.024850C(26) -0.031382 0.024638C(27) 0.005233 0.010120


Table 4.15: RINTC = C(28)* HPC(-1) + C(29)* HPC(-2) + C(30)*POPGC(-1) + C(31)*POPGC(-2) + C(32)*GDPC(-1) + C(33)*GDPC(-2) + C(34)*RINTC(-1) + C(35)*RINTC(-2) + C(36)


26

Normalized Restriction (= 0) Value Std. Err.C(28) 0.045137 0.040117C(29) -0.019883 0.041052C(30) 0.257252 0.202831C(31) -0.286856 0.200032C(32) -0.217697 0.392468C(33) 0.959752 0.397934C(34) 0.831894 0.071741C(35) -0.237376 0.071131C(36) 0.012537 0.029217


4.3.2.3: Impulse Response Function Results of VAR Model

Cholesky decomposition is used for examining the response of the dependent variables to

independent variables in all the four models. The results are given in figure 4.2 as follows.

Figure. 4.2: Impulse Response Function Results

27

The results show that housing price index in the first model is affected by the shocks from all the

explanatory variables i.e. lag housing price index, population growth rate, Gross Domestic

Product and real interest rate. Similarly, the models of population growth rate, Gross Domestic

Product, real interest rate also shows the affect of the shocks on the dependent variables from the

independent variables.

Conclusion

We have examined the role of the Federal Reserve monetary policy in the US housing price

boom. The specific focus of the study was on to investigate the role of nominal and real interest

rate in the determination of the US housing prices.Monthly data for the period January-2000 to

December- 2015 has been used for the empirical estimation of the results. Hodric Prescott Filter

method has been used for the stationarity of the data. Moreover, Ordinary Least Squares method,

Granger Causality test and Vector Auto Regression has been applied for the computation of the

results.

Overall, the results showed that nominal interest alongwith inflation and population growth rate

does contribute to the US housing Price rise. Whereas, the relationship between the real interest

rate and housing price index seems to be ambiguous. It is suggested that the Federal Reserve can

used nominal interest as an effective tool for controlling the US housing price boom.

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