Influence AEX index on Dutch residential house prices

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Influence AEX index on Dutch residential house prices

31 January 2018 Jelle Westerbos 10755470

Thesis Supervisors: Drs. P.V.Trietsch and Drs. P.J.P.M Versijp University of Amsterdam

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Statement of Originality

This document is written by Jelle Westerbos who declares to take full

responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and

that no sources other than those mentioned in the text and its references have

been used in creating it.

The faculty of Economics and Business is responsible solely for the supervision

of completion of the work, not for contents.

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Abstract

This study examines the relationship between the change in Dutch house prices and the change between the AEX index during the period 1985 till 2016 using quarterly and yearly data. Outside the Netherlands several studies found a positive relationship between the change in stock prices and the change in house prices. Two procedures were used for this analysis. First a linear multiple regression analysis (OLS) has been used to estimate the relationship between the change in AEX price and the change in Dutch house prices. Alternatively a multiple regression analysis has been used to estimate the relationship between the change in AEX price and the change in different types of Dutch houses.

The results show that only the detached house prices are significant positively correlated with the change in the AEX index price. Further research in this field with more variables and models is suggested.

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Chapter 1 Introduction

1.1 Problem

In countries outside the Netherlands, research has been conducted on the correlation between the stock market and house prices. For example, Ali & Khalid (2017) have been researching the influence of house prices on stock prices in 22 European Union countries, excluding the Netherlands. So far, there has been little or no research in the Netherlands on the relationship between the Dutch equity index "AEX" and Dutch house prices. The only Dutch research found is of Kakes & Van Den End (2004), where a positive correlation between stock prices and house prices was found. The research on this topic was before 2004, it is possible to find other outcomes when the period after 2004 is included in the research. If the research shows that equities have a positive correlation with house prices, this could give an indication of the direction where the house prices could go. Different stakeholders such as investors and families or institutions investing in houses could benefit from this as they have houses in their portfolios.

1.2 Research question

Using literature and a statistical procedure including regressions the following question will be answered.

Does the AEX index significantly affects the Dutch residential house prices between 1985 and 2017?

The following sub-questions will help finding an answer on the research question. What are the findings of other researchers?

Which factors are affecting the residential house prices in the Netherlands?

Has the change in the AEX index different influence on the change in price for various house types?

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1.3 Literature

According to Kakes & Van Den End (2004) equity has influence on the Dutch housing prices. This connection is strongest for the more expensive segment of the housing market. Ghulam Ali & Khalid Zaman (2017) investigate the long-run and causal relationship between house prices and stock prices in the panel of 22 European Union countries. This paper gives mixed conclusions, for several countries which show a negative correlation between house prices and stock prices and other countries a positive relationship. GDP, interest rate, population growth and construction costs, house rent and house sales are all factors besides stock indexes that influence house prices according to Tsatsaronis & Zhu (2004) and Jud & Winkler (2002).

1.4 Data

The data used in this research will all be Dutch quarterly or yearly data from 1985 till 2016. For the house types yearly median data will be used and for the total houses quarterly median data. The following variables will be measured in the research, where the variables are changes in year over year or quarter over quarter: median house prices, AEX index price, gross domestic product, population growth, real interest rate, rent price, house sales and house construction costs. The data gathered is of a long-term period, otherwise the last financial crisis could have a too big impact on the statistical results.

1.5 Method

The relation between the AEX index and the Dutch housing market will be investigated with the help of a linear multiple regression analysis between 1985 and 2016 which includes the variables mentioned in section 1.4. Furthermore five regressions will be performed for the different house prices. In addition, the assumptions for the regression model will be checked. The STATA program will be used for the statistical procedure.

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1.6 Structure

Before starting with the statistics of this research, the theory behind the AEX, Dutch housing market and the different factors which are assumed to affect the housing prices will be explained in the literature review. Following the literature review, the methodology will be explained. Here the data found will be extensively explained. This will be followed by the hypotheses and the multiple regression model. Following the methodology, the empirical results will be discussed. At last the main findings and limitations of the research are discussed.

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Chapter 2 Literature review

In this chapter past research of the determinants of house prices will be discussed. First papers of various research about house price determinants will be discussed. The next section will explain the individual house price factors found: Stock market, gross domestic product, population growth, real interest rate, rent price, house sales and house construction costs.

2.1 House price determinants

The supply and demand of houses is determined by specific factors, which finally determine the house prices according to Tsatsaronis & Zhu (2004). This are growth in income, interest rates, inflation, shifts in population and construction costs. Jud & Winkler (2002) find in a research on the dynamics of housing prices in the US the real housing price is strongly influenced by the growth of population , changes in GDP, house construction costs and interest rates. Tsai (2002) studies the demand and supply side of house prices and finds that rent, construction costs and the number of house sales have an effect on the house price.

2.2 Relevant factors affecting house prices

Stock market

The stock market is part of the global capital market. Companies can list their shares at exchanges to be traded between investors. In this research the most important factor on the change in house prices is the change in the AEX index price. The Dutch stock market consists of three indexes: The AEX, AMX and the ASCX. The AEX index is the most important indicator of the Dutch stock market which consists of 25 companies. The AMX and the ASCX consists of mid-cap and small-cap companies. The AEX index price is derived

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from the weighted average price of the company stocks listed on this exchange. The AEX is the most used indicator for the Dutch stock market (Euronext, 2017)

Quan & Titman (1999) finds significant positive relations between real estate returns and stock returns. Their results suggests that a fraction of the observed positive correlation is due to economic fundamentals that affect both real estate returns and stock prices,

specifically GDP growth. Since the year 2000 there have been two stock market crashes. The Dotcom bubble from 1997 – 2000 and the Global financial crisis of 2007 – 2008. A time-varying analysis of the correlations between house prices and stock market indices found relatively high correlations during the global financial crisis. Dirk and Heaney (2017).

In another study of Ali & Zaman (2017) the stock prices and house prices of 22 European countries were examined between January 2007 and October 2012. Five of the countries showed a negative relationship between change in house prices and stock prices, the rest of the countries showed a positive relationship. The study also finds that the change in stock market returns also has a strong lagged effect on house prices.

According to the BIS Annual report (BIS 73rd_{annual report, p. 117), the AEX data is} lagged by two and a half years on average, because generally house prices are not affected immediately by changes in de AEX index price. In another study by Sutton (2002) he finds the house price changes are significant due to a stock market shock 8 to 12 quarters before the price change.

According to Cocco (2005) households with a higher expected future income are more likely to buy larger houses and are also willing to invest a larger share of their financial wealth in stocks. When the AEX index price increases/decreases the population with a higher net worth, higher income and accordingly a larger house tend to be more related to the stock market. There is a possibility the price of detached (larger) houses is more correlated with the AEX price index.

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Gross domestic product

Gross domestic product is the sum of all goods and services produced and provided within a countries border within a specific timeframe. According to Ioannides (1997) the GDP growth has a positive significant effect on the change in house prices and is significantly positively correlated with household income. The paper compares the dynamics of housing prices in 15 OECD countries. He finds a high degree of similarities across the different countries,

although the Netherlands is not included. On another note Filis (2010) shows that GDP is acting as the leading indicator of stock market movements, what leads to a growth in the stock market when there is growth in GDP.

Population growth

Found is that a high population growth generally increases house prices due to more demand for housing supply. According to Steen, Pellenbarg, & Groote (2016) are Dutch households moving to cities since early 90’s to 2015. A high growth of the population in cities, could indicate an increase in the price of houses in the cities. On the other hand, a total increase in population increases demand for houses, what increases the house prices accordingly.

Mankiw and Weil (1989) found that the population growth during the baby boom increased house prices. They found that changes in the number of births lead to large and predictable changes in the demand for housing , which appears to have a substantial in impact on the house prices. The millennial generation will continue to drive the growth of housing demand, although this could be lower due to a lower growth of population compared to the baby boom period.

Real interest rate

The real interest rate in this research is the interest rate of ten year Dutch treasury bonds minus the inflation, this to remove the effects of inflation. Generally a low interest rate encourages consumers to spend more. According to Harris (1989) interest rates are normally

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negatively correlated with the change in house price, when the interest rate decreases, house prices increase. He finds this is not always the case. In the 70’s house prices increased even when interest rates increased. Harris (1989) also finds real interest rate is better to reflect changes in expectations than the nominal interest rate. Demary (2010) researched the linkage between real interest rate and the house prices of 10 OECD countries by applying

vectorautoregressions. He finds that interest rate shocks lower real house prices and explain 12 – 24 percent of the variation in house prices.

Rent price

The rent price affects the demand side of the housing market. According to Tsai (2012) When the house prices significantly increases faster than the rent prices or vice versa a housing price bubble exists, which means the two variables should be correlated most of the time. House sales

House sales or transactions is the transfer of ownership from one person or entity to another person or entity. According to Andrew & Meen (2003) house sales and the house price have a strong relationship, but due to greater financial inequality in the world the relationship could change significantly.

House construction costs

While the rent price is a factor affecting the demand, construction costs affect the housing supply. In times when the construction costs are low, supply of houses increases, what should have a negative effect on the house prices Tsai (2012). The construction costs are determined by labor costs and the costs of materials.

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2.3 Hypothesis

Two hypotheses will be tested in this research.

- The first hypothesis will test whether the change in the AEX index price has a positive effect on the change in quarterly Dutch house prices

- The second hypothesis will test whether the change in the AEX index price has a positive effect on the yearly Dutch house type1_prices

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Chapter 3 Methodology

3.1 Data

The data for the Median house price, AEX index price, GDP, population growth, real interest rate, rent price and number of house sales has been retrieved from Datastream. Construction costs are retrieved from CBS Statline database. For the regression of the change in median Dutch house prices quarterly data is used for all variables for the period between 1985 and 2016 with 128 observations. Most relevant studies use quarterly data as well. For the five regressions of change in median Dutch house prices the median prices of the five house types are from the Dutch association of real estate agents (NVM), construction costs are from the CBS Statline database and the AEX index price, GDP, population growth, real interest rate, rent price and number of house sales are retrieved from Datastream, with 31 observations per variable. No monthly or quarterly data was available for the different house types, yearly data was the only available data. Kakes & Van Den End (2004) mentioned in the introduction researched the period from 1985 till 2002, where this research also takes the period from 2002 till 2016 into account.

3.2 Model specification

The objective of this research is to find whether there exists a significant positive relationship between the change in the AEX index price and the change in house price.

An OLS linear multiple regression analysis will be performed to find if there exists a significant relation between these factors with the change in house price as the dependent variable and change in AEX index price, change in GDP, change in population growth, real

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interest rate, change in rent price, change in house sales and change in construction costs as control variables.

Two sets of regressions will be performed: One regression with the change of the median Dutch house prices as dependent variable. Five other regressions with the median Dutch house prices as dependent variable for the effect on different types of houses. The following model will be used to estimate the effect of the change in AEX price on the change in the house prices. In this model 𝛽" will be tested if this is significantly higher than

zero. For all regressions this model will be used. In the regression for the total change in house prices a quarterly time frame is used for all variables. The regressions with the change in house prices for different types of houses a yearly time frame is used for all variables.

𝐻𝑃𝑅_& = 𝛼 + 𝛽_"𝐴𝐸𝑋_&+ 𝛽_.𝐺𝐷𝑃_& + 𝛽₁𝑃𝑂𝑃_&+ 𝛽₃𝑅𝐼𝑅_& + 𝛽₅𝑅𝐸𝑁𝑇_& + 𝛽₈𝐻𝑆𝐴_& +

𝛽_:𝐶𝑂𝑁𝑆𝑇𝑅_&+ 𝜀_&, (1)

Where:

𝐻𝑃𝑅& = Change in median house prices;

This variable is the change in median house prices, quarterly for the regression on the change in median total Dutch house prices and yearly for the regression on the change in house prices for various types of Dutch houses.

𝐴𝐸𝑋_&= Change in AEX index price;

The AEX is expected to be positively correlated with the house price Quan & Titman (1999) and Ali & Zaman (2017). As in previous research Sutton (2002) the AEX price index will be lagged by 10 quarters.

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The change in GDP is expected to be positively correlated with the change in house price, since this could be used as an indicator of income according to Ioannides (1997). As more or less money could be invested, this could significantly affect the house price.

𝑃𝑂𝑃_& = Change total population;

Expected is that the change in population positively affects the house price. As the population grows, this increases the demand for houses, increasing the house price Jud & Winkler (2002).

𝑅𝐼𝑅& = Real interest rate;

The treasury bond interest rate is used less the inflation rate. Expected is that the change in house price is negatively correlated with the real interest rate. For example, when the real interest rate decreases, lending will be cheaper and the demand for houses is expected to increase, what will lead to an increase in the house price.

𝑅𝐸𝑁𝑇_& = Change in rent price;

Expected is that the change in rent price is positively correlated in the long term with the change in the house price, because the cost of housing is also increases. Alternatively in the short term rent could be negatively correlated with the change in house price, because when the rent increases it could be cheaper to buy a house Todd & Souleles (2005).

𝐶𝑂𝑁𝑆𝑇𝑅& = Change in construction costs;

Supposed is that the change in construction costs are positively correlated with the change in house prices. When the construction costs decrease this has a positive effect on the supply of houses, what decreases the house price according to Tsai (2012).

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𝐻_>: 𝛽_" ≠ 0 The AEX index has no effect on the Dutch house prices

𝐻_": 𝛽_" > 0 The AEX index has a positive effect on the Dutch house prices

𝐻_>: 𝛽_" ≠ 0 The AEX index has no effect on the Dutch house type prices

𝐻_": 𝛽_" > 0 The AEX index has a positive effect on the Dutch house type prices

The null hypothesis will test whether the 𝛽_" is significantly different from zero. When 𝛽_" is significant, the change in the AEX index price is positively correlated with the change in the house price. This applies to both sets of regressions.

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Chapter 4 Empirical results

The purpose of this research is to investigate if the change in the AEX index price

significantly affects the change in the Dutch house prices. This investigation has been divided into two parts. The first part tests whether the change in AEX index price has significant effect on the change in the total Dutch house prices. The second part tests whether the change in the AEX price has a significant effect on the change of various house type prices.

In this chapter the results of the research will be presented and discussed. For both hypotheses the following will be discussed separately. First the descriptive statistics will be discussed. Second the results of the various regressions will be discussed. Third the

assumptions of a linear multiple regression analysis will be presented and discussed, namely the multicollinearity and heteroscedasticity.

4.1 Results and interpretation

Hypothesis one Descriptive statistics

Table 4.1 represents the descriptive statistics for the variables of the first hypothesis with quarterly data. The minimum of 9.18% in the change in house price is in Q1 2009 during the financial crisis. This is not perfectly in line with the change in AEX lagged with ten quarters, but approximately five quarters. Alternatively during quarter of Black Monay in 1987 the AEX price decreased with 35.999%, exactly ten quarters later the house price decreased with 6.2%, the following quarter a decrease of 3.5% and after that quarter an 2.5% decrease. This is in line with the 10 quarter lag from the research of Sutton (2002).

An interesting observation is that the average change in house price is negative. For most of the timeframe the increase in the change in house prices was relatively constant, the mean could be negative due to the two house price crashes in the timeframe. As is expected

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the house price change volatility is considerably lower than the AEX price change volatility. The largest decline in GDP is during the financial crisis in Q1 2009. The change in rent price is relatively stable compared to the other variables, this could be due to the gradual and relatively stable increase in house price over the years. The maximum real interest rate was in 1987, at the time when the inflation was also relatively high, furthermore the house price change during that period was also high. This could be an effect of the high increase of population during that period. Observed is that the change in house transactions is relatively stable during the timeframe.

Table 4.1: Descriptive statistics total houses

Variable Mean S.D. Minimum Maximum

𝐻𝑃𝑅 -0.00719 0.02668 -0.09179 0.06451 𝐴𝐸𝑋 0.02443 0.11459 -0.35999 0.32746 𝐺𝐷𝑃 0.00568 0.00726 -0.03170 0.02450 RENT 0.00779 0.00404 -0.00375 0.02535 POP 0.00129 0.00040 0.00036 0.00198 𝑅𝐼𝑅 0.03003 0.02177 -0.01220 0.07360 HSA 0.00250 0.00023 0.00211 0.00297 CONSTR 0.00514 0.01825 -0.05091 0.06361

Table 4.1 represents the descriptive statistics of the variables used in the regression for hypothesis 1. All quarterly variables, for the period

1985-2016. observations per variable: 128

Results

The results of the regression presented in table 4.2 are interpreted in the following way. When the change in the AEX price increases by one percent the change in the house price increases by 0.196%. This result is expected as the two variables are positively correlated and the AEX price is more volatile. The p-value of the change GDP and change in rent are the only significant variables. GDP is significant at the 10% level and rent is significant at the 5% level and the 10% level. For GDP and rent this in line with the literature as Todd & Soulales (2005) find the change in rent has a positive correlation with the change in house prices. A 1% increase in the change of GDP indicates a 0.5695% increase in the change of

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house prices, this is in line with research of Ioannides Y.M. (1997). The change in AEX price is not significant when the change in house prices are corrected for inflation, when the change in house prices is not corrected for inflation the change in AEX price is significant at the 10% level. For this regression the 𝑅._{= 10.18%, this indicates that the variance in the}

change in detached house prices is explained for 10.18% by the independent variables in the model. This is rather low compared to other studies. This could be explained by the limited number of variables.

Table 4.2: Regression results total houses

Variable Coefficient t-statistic p-value

AEX 0.0196 (0.0206) 1.07 0.287 GDP 0.5695 (0.3465) 1.85 0.067 RENT 1.1063 (0.6004) 2.08 0.04 POP 8.8330 (6.7116) 1.48 0.141 RIR -0.2318 (0.2555) -1.02 0.309 HSA 24.8253 (25.5909) 1.09 0.276 CONSTR -0.16663 (0.1339) -1.40 0.164

Table 4.2 represents the estimation results from the regression of hypothesis 1 with a 𝑅._of_0.1018

Hypothesis two Descriptive statistics

Table 4.4 represents the descriptive statistics for the variables of the second hypothesis with yearly data. The minimum of 9.18% in the change in house price is in Q1 2009 during the financial crisis. This is not perfectly in line with the change in AEX lagged with ten quarters, but approximately five quarters. Alternatively during quarter 3 of Black Monday in 1987 the AEX price decreased with 35.999%, exactly ten quarters later the house price decreased with 6.2%, the following quarter a decrease of 3.5% and after that quarter an 2.5% decrease. This is in line with the 10 quarter lag from the research of Sutton (2002). As is expected the house

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price change volatility is considerably lower than the AEX price change volatility. The largest decline in GDP is during the financial crisis in Q1 2009.

Table 4.4: Descriptive statistics Detached houses

Variable Mean S.D. Minimum Maximum

𝐻𝑃𝑅 0.02940 0.06427 -0.13036 0.17802 𝐴𝐸𝑋 0.08501 0.21703 -0.37207 0.55582 𝐺𝐷𝑃 0.03954 0.02369 0.03380 0.08050 RENT 0.03164 0.01070 0.01648 0.05394 POP 0.00521 0.00175 0.00146 0.00791 𝑅𝐼𝑅 0.02935 0.13173 -0.00550 0.07060 HSA 0.04489 0.13173 -0.37232 0.36347 CONSTR 0.02158 0.03593 -0.04900 0.10900

Table 4.4 represents the descriptive statistics of the variables used in the regression for hypothesis 2. All yearly variables,

for the period 1985-2016. observations per variable: 31

The results of the regression are interpreted in the following way. When the change in the AEX price increases by one percent the change in the house price increases by 0.465%. This result is expected as the two variables are positively correlated and the AEX price is more volatile. For the different house types, the p-value of the change in AEX price is only significant for the 5% level for the detached house type. According to Cocco, J. (2005) people who buy bigger houses on average put relatively more of their financial assets in stocks compared to owners of smaller houses. Detached houses are generally bigger houses than apartments, terraced houses, corner houses and semi-detached houses. This could be the reason the change in AEX index price of the regression for detached houses is significant. The GDP is in all regressions significant at the 5% or 10% significance level, except for terraced houses. For detached houses a 1% increase in the change of GDP indicates a 1.21% increase in change of house prices, Ioannides Y.M. (1997) For this regression the 𝑅.₌

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for 68.07% by the independent variables in the model. Compared to hypothesis one this is considerably higher, this could be explained by the year data.

Table 4.5: Regression results change price house types

Apartments Terraced

houses

Corner houses Variable Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient

t-statistic p-value AEX 0.0465 (0.0286) 1.63 0.118 0.0396 (0.0341) 1.16 0.258 0.0355 (0.0337) 1.05 0.304 GDP 0.9328 (0.3145) 2.97 0.007 0.6534 (0.3927) 1.66 0.110 0.7178 (0.3914) 1.83 0.080 RENT 0.3999 (0.6864) 0.58 0.566 0.5343 (0.7751) 0.69 0.632 0.4059 (0.7708) 0.53 0.604 POP 4.9691 (4.0840) 1.22 0.236 2.3576 (4.8520) 0.49 0.632 1.4455 (4.7985) 0.30 0.766 RIR 0.1240 (0.3065) 0.40 0.690 0.1350 (0.3675) 0.37 0.717 0.1774 (0.3652) 0.49 0.632 HSA 0.1643 (0.0651) 2.52 0.019 0.1131 (0.0791) 1.43 0.166 0.1612 (0.0838) 1.92 0.067 CONSTR 0.1384 (0.2193) 0.63 0.534 0.3216 (0.2684) 1.20 0.243 0.3152 (0.2705) 1.16 0.256

Table 4.5 represents the estimation results from the regression of hypothesis 2

Table 4.5: Regression results change price house types

Semi-detached houses

Detached houses

Variable Coefficient t-statistic p-value Coefficient t-statistic p-value

AEX 0.0450 (0.0395) 1.14 0.268 0.0831 (0.0378) 2.19 0.039 GDP 0.9978 (0.4630) 2.15 0.042 1.2087 (0.4382) 2.76 0.011 RENT 0.2874 (0.8778) 0.33 0.746 -0.0074 (0.82091) -0.01 0.993 POP 1.9721 (5.6256) 0.35 0.729 0.6654 (5.3617) 0.12 0.902 RIR 0.2174 (0.4301) 0.51 0.618 0.4959 (0.4122) 1.20 0.241 HSA 0.1259 (0.0842) 1.50 0.148 0.1834 (0.07060) 2.60 0.016 CONSTR 0.2532 (0.3189) 0.79 0.436 0.2159 (0.3074) 0.7 0.489

Table 4.5 represents the estimation results from the regression of hypothesis 2

4.2 Assumptions for multiple regression analysis

Test for multicollinearity

To test for multicollinearity a variance inflator factor (VIF) test is used, this is calculated with the following formula: VIF = _"P Q"

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when VIF ≥ 4 there is a high possibility of multicollinearity. In table 4.4 the VIF values generated by Stata of house sales and real interest rate have a value higher than four, what indicates these variables have a high chance of multicollinearity. In the correlation matrix the two variables have a correlation of 0.9021.

Table 4.7: VIF table

Variable VIF 1/VIF

𝐻𝑆𝐴 6.76 0.14785 𝑅𝐼𝑅 5.81 0.17211 POP 1.55 0.64314 GDP 1.19 0.84201 𝐶𝑂𝑁𝑆𝑇𝑅 1.12 0.89120 RENT 1.10 0.90700 AEX 1.05 0.95474 Mean VIF 2.66 Table 4.7

For the change in detached house prices an additional VIF test is performed. This table indicates no multicollinearity as the VIF ≤ 4 for all variables. It is possible the house sales and real interest rate are still highly correlated, because only yearly data is used for these variables.

Table 4.8: VIF table detached houses

Variable VIF 1/VIF

𝐻𝑆𝐴 2.13 0.46994 𝑅𝐼𝑅 1.88 0.53181 POP 1.54 0.64864 GDP 1.51 0.66259 𝐶𝑂𝑁𝑆𝑇𝑅 1.40 0.71678 RENT 1.35 0.74342 AEX 1.18 0.84818 Mean VIF 1.57 Table 4.8

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Test for heteroscedasticity

To check whether there exist heteroscedasticity in the models a White test is performed in Stata. Variance in the errors has to be constant for the model to be valid, this means the alternative hypothesis has to be rejected. The following hypothesis will be used.

H0: presence of homoscedastic errors H1: presence of heteroskedastic errors

By performing the White test a value of 0.2484 for the change in total house prices and a p-value of 0.4154 is found for the detached house type. This leads to not rejecting the null hypothesis using a significance level of 10% for both regressions.

Another test is performed in Stata, performing the exact same regressions again, but with standard robust errors included. Table 4.6 and 4.7 represent the regression results with the standard robust errors. For the change in the price of detached houses the p-value for the change in the AEX index price is 0.071, still significant at the 10% level, but not significant at the 5% level compared to the original regression results. For the change in the price for the total houses the p-value is 0.294, still insignificant at the 10% level.

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Chapter 5 Conclusion and limitations

Conclusion

This study investigated the relation between the change in house prices for the total Dutch housing market and the change in house prices for different types of houses. Previous

research has found a significant positive correlation on the relationship between the change in house prices and the change in stock market price in most countries (e.g. Ali and Khalid, 2017; Quan and Titman, 1999) and Kakes and Van Den End (2004) found a significant positive relation between the Dutch stock market and the house prices in the period 1983 till 2004. This investigation also investigated the period from 2005 till 2016, with a simplified model. In this study a multiple linear regression model has been set-up in order to investigate the empirical relationship between these variables. GDP, population growth, real interest rate, rent price, number of house sales and construction costs have been included in the model as control variables. Six regressions were performed with these variables. One regression with the change in median house prices for total Dutch house market as the dependent variable. Five regressions with the change in median house prices for various types of houses. Only for the regression with the change in median house prices for detached houses a significant positive relationship between the change in AEX price and the change in detached house prices has been found. A 1% increase in the change of AEX index is estimated to increase the change in detached house prices by 0.465%. This results could be explained by the financial portfolio of the owners of detached houses. According to Cocco, J. (2005) people who buy bigger houses on average put relatively more of their financial assets in stocks compared to owners of smaller houses. The possibility exists when the AEX increases or decreases the worth of the financial portfolio of these house-owners, the house owners have an incentive to buy or sell their detached house faster than owners of other types of houses.

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Limitations

There are limitations in the models and data used in this research. The housing market is bound to many more independent variables than used in this model. Different demographic variables, taxes and regulations and land prices were not included in the model. For a

research in this form it is not possible to include all different variables. Furthermore the data of the different house types is all yearly, because monthly/quarterly prices for the house types are not available. This resulted in a relatively small sample size. The results of the regressions related to the different house types can because of this only be seen as an indication of the results that can be generated with more data. Other models could have been used such as a vector autoregression model or a Johansen cointegration test.

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Apendix

Correlation matrixes

Table 4.3: Correlation Matrix total houses

Variable HPR AEX GDP RENT POP RIR HSA CONSTR

𝐻𝑃𝑅 1.0000 AEX 0.0730 1.0000 GDP 0.2296 0.0543 1.0000 RENT 0.0967 -0.0579 0.0362 1.0000 POP 0.0724 0.0021 0.0086 0.2566 1.0000 RIR 0.1995 0.0830 0.3112 0.1691 0.3810 1.0000 HSA 0.1892 0.1375 0.2730 0.2091 0.5113 0.9021 1.0000 CONSTR -0.0813 0.0460 0.2359 -0.0520 0.1997 0.1531 0.1693 1.0000

Table 4.3 provides a correlation matrix for the variables of the regression

Table 4.6: Correlation Matrix Detached houses

Variable HPR AEX GDP RENT POP RIR HSA CONSTR

𝐻𝑃𝑅 1.0000 AEX 0.3937 1.0000 GDP 0.7093 0.2313 1.0000 RENT 0.2418 -0.0156 0.1629 1.0000 POP 0.2645 0.2020 0.1910 0.3526 1.0000 RIR 0.3524 0.1710 0.3161 0.2407 0.4156 1.0000 HSA 0.3642 -0.1749 0.2016 0.3139 -0.0414 -0.1381 1.0000 CONSTR 0.4452 0.3592 0.5537 0.0841 0.4218 0.3379 -0.2321 1.0000

Table 4.6 provides a correlation matrix for the variables of the regression

Robustness tables

Table 4.9: Robustness check detached houses

Variable Coefficient estimate p-value

𝐴𝐸𝑋 0.0831 (0.04388) 0.071 𝐺𝐷𝑃 1.2087 (0.4390) 0.011 𝑅𝐸𝑁𝑇 -0.0074 (0.5978) 0.990 𝑃𝑂𝑃 0.6653 (5.3647) 0.902 𝑅𝐼𝑅 0.4959 (0.3954) 0.222 𝐻𝑆𝐴 0.1834 (0.0561) 0.003 CONSTR 0.2159 (0.3887) 0.584

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Table 4.10: Robustness check total houses

Variable Coefficient estimate p-value

𝐴𝐸𝑋 0.0195 (0.0185) 0.294 𝐺𝐷𝑃 0.5695 (0.4111) 0.169 𝑅𝐸𝑁𝑇 1.1062 (0.4195) 0.009 𝑃𝑂𝑃 8.8330 (5.8508) 0.134 𝑅𝐼𝑅 -0.2318 (0.2326) 0.321 𝐻𝑆𝐴 24.8252 21.9835) 0.261 CONSTR -0.16662 (0.1094) 0.131

Influence AEX index on Dutch residential house prices