interest rate and housing price a case study in france

TABLE OF CONTENT LIST OF TABLE LIST OF GRAPH CHAPTER 1: INTRODUCTION CHAPTER 2: LITERATURE REVIEW 2.1 Fundamental determinants of house prices 2.1.1 GDP 2.1.2 Bank credit/Mortgage loan 2

FOREIGN TRADE UNIVERSITY

HO CHI MINH CITY CAMPUS

TOPIC: INTEREST RATE AND HOUSING PRICE

A CASE STUDY IN FRANCE

Lecturer: Lé Hang My Hanh Class: KS9CLC3

Phạm Nguyễn Mỹ Dung 2011155102

Ho Chi Minh, 29% April, 2022

TABLE OF CONTENT LIST OF TABLE

LIST OF GRAPH

CHAPTER 1: INTRODUCTION

CHAPTER 2: LITERATURE REVIEW

2.1 Fundamental determinants of house prices

2.2 VAR literature review

CHAPTER 3: METHODOLOGY AND DATA

3.1 Data collection and specification

3.2 Data description and modification

4 Empirical results and testing

4.1 VAR estimation, interpretation and Granger Causality test

4.2 Impulse - Response Function (IRF) and Variance Decomposition

LIST OF TABLE Table 2.1 Selected empirical studies on the determinants of house prices 11 Table 3 1 The summary of collecf€d (afa óc << SH HH HH HH HH, 16 Table 3 2 Summary of data affer modifiCafiOI - chà 1H Hi, 17 Table 3.3 A brief result for the Dicky-Euller f€SẲ 5: +5 «se k+k series 18 Table 4.1 Identifying the optimal lap lengttH - 5: S11 S*S SH HH Hy 20 Table 4.2 Granger €CausallfV f€SÍ <1 S1 HH KH HH ĐH HH HH, 21 Table 4.3 Lagrange-multiplier (Breusch-GodÍrey) †€SK key 22 Table 4.4 VAR stability {€SE SÁT“ HH TH HE TH HE TH HH TH Khv 22

LIST OF GRAPH

Graph 4 I The graph of IRF (response öoƒ HPI onh)) Graph 4 2 The graph of IRF (Impulse: STIR, Response: HPI) Graph 4 3 The Variance decomposition on IRF

CHAPTER 1: INTRODUCTION

Following the financial crisis of 2007-2008, the commercial real estate market received renewed attention Indeed, a strong correlation between commercial property prices and the banking crisis prompted several financial stability institutions to launch projects aimed at better understanding the commercial real estate market Residential properties are one of the four main sub-markets of commercial real estate (offices, retail, industrial buildings, and residential properties), and their prices are strongly linked to economic junctures and financial risks To detect potential housing imbalances and improve financial stability, it is critical to understand the role of the housing sector in the past, as well as the links between housing prices, monetary policy, and macroeconomic activity

This paper aims to quantify the effect of monetary policy and some other macroeconomic factors on house prices in France This is motivated by our interest in the fundamental drivers of house prices in the long-run, and how the monetary policy influences housing prices through the level of interest rates Understanding the house price dynamics in France makes for a very interesting case study to assess the relevance

of some fundamental factors, as some interrelationship between them appear to be puzzling

The question of the research is how interest rate affects the housing prices with respect to some fundamental factors in France?

In fact, the error correction model or DOLS methodology has been widely used

in previous research on French housing prices (Hofmann, 2003; Bald4zs Egert and Dubravko Mihaljek, 2007; Adams and Fiiss, 2010) In our paper, however, we use the VAR model, which allows for interaction effects between real housing prices and the related fundamentals We also examine typical house price responses to unforeseeable changes over time, i.e "shocks," in terms of their key determinants within the context

of the VAR model In addition, The Granger causality test and impulse responses are also used to determine the relative importance of each predictor in our estimates The remainder of this paper is organized as follows: Section 2 focuses on the literature review that presents existing studies which has investigated the effect of fundamentally dependent variables on housing prices Section 3 includes the data collected, as well as the analysis on the empirical result using the VAR model Section

4 is the result presentation as well as interpretation and discussion Finally, section 5 provides the conclusion

CHAPTER 2: LITERATURE REVIEW

2.1 Fundamental determinants of house prices

There exists a huge amount of related literature and studies on determinants of housing prices Accordingly, most of the studies derived the housing prices from the function of demand and supply, which means the long-run equilibrium of housing prices can be explained by some fundamental demand-side factors including GDP, disposable income, interest rates, mortgage credit, demographic; and the supply-side factors including housing stocks and construction cost

2.1.1 GDP

Real GDP is largely acknowledged as one factor among the primary long-run macroeconomic driver of housing prices Theoretically, GDP can provide the state of the business cycle and household income, which in turn reflects the household’ s affordability for houses Using panel DOLS methodologies, Balazs Egert and Dubravko Mihaljek (2007) discover a substantial positive association between GDP and housing prices in eight transition economies of Central and Eastern European and 19 OECD countries in the period of 1995 — 2005, regardless the dynamics of different country groups When Xu and Tang (2014) use a cointegration and ECM to look at determinants

of UK house prices from 1971:Q1 to 2012:Q4, they come to the same conclusion Hideaki Hirata, M Ayhan Kose, Christopher Otrok, and Marco E Terrones (2012) examine the house prices in 18 advanced OECD countries for the period 1971:Q1 to 2011:Q3 and find the co-movement between housing prices and the aggregate output (GDP), with housing prices soaring during expansions and declining during recessions However, the degree of such co-movement varies country by country, with the least in Australia and Canada and the most in Finland and the United Kingdom During the survey period, the average correlation between house prices and output is close to 0,5

2.1.2 Bank credit/Mortgage loan

Mortgage loan is among the demand-side factors that gains a lot of attention in empirical studies According to Bernanke and Gertler (1989), house prices and mortgage lending are interdependent variables, which has an effect on the other through a so-called

“financial accelerator= mechanism The loosening of bank mortgage loans can stimulate higher demand for housing purchases, which in turn causes the housing prices to increase When housing prices go up, which also increases the pledged collateral in banks’ balance sheets, banks now can extend additional mortgage loans and further stimulate the demand for houses, and hence, raise the housing prices The two-way relationship between housing price and bank credit is also recorded in the empirical paper (Hofmann, 2001), which uses the multivariate empirical approach within the scope

of 16 industrialized countries Hofmann et al (2003) investigate the dynamic patterns of the interrelationship of the two variables, which are bank credit and property prices, in

20 advanced economies from 1980 to 2002 He claims the changes in real bank lending have a significantly positive effect on property prices in six countries, with the pooled estimate being significant at the 1% level in the Error Correction model (ECM) 2.1.3 Interest rate

In most previous studies, the interest rate is found to have a significant influence

on housing prices and has an expected negative sign Theoretically, interest rate represents the cost of borrowing, so an increase in interest rate can discourage potential buyers from taking out a loan to fund their house purchase, causing a fall in house demand and housing prices, and vice versa (Apergis and Rezitis, 2003; Igan et al, 2011; Andrews, 2010) According to Hilbers et al (2008), interest rates play a dual role in the European housing market The first is the mortgage rate, which indicates the cost of borrowing, and the second is the risk-free rate, which determines the opportunity cost Barot and Yang (2002) examine the real interest rate and conclude that actual housing prices in the UK are inversely correlated to the real interest rate, whereas house prices in

Sweden are more subject to changes in the real interest rate Adams and Fiiss (2010) investigate the long-term impacts and short-term dynamics of macroeconomic fundamentals on housing prices using ECM and data for 15 OECD nations from 1975 to

2007 They discover that a 1% increase in long-term interest rates causes housing prices

to drop by 0.36 percent in the long run in Panel Group DOLS, which is significant at 1% level In the study by Baldzs Egert and Dubravko Mihaljek (2007), they find the negative coefficients of interest rate on the house prices vary from -0,002 to -0,015 in eight transition economies of Central and Eastern European and 19 OECD countries, which is

at the 5% significance level

2.1.4 Disposable income

LJ.M de Greef and R.T.A de Haas (2000), had completed some research in terms

of the link between home prices and mortgage lending by looking at the Dutch situation

In brief, they argue in their study's conclusion that disposable income was shown to be reliant on the housing price as well as mortgage lending, and that a 1% rise in real disposable income would result in a 2.1 percent increase in the real housing price On the other hand, Kishor, N Kundan; Marfatia, Hardik A (2017) have revealed a study in which the objective is to investigate the dynamic relationship among house prices, income, and interest rates in 15 OECD countries As a result, personal disposable income

is mostly affected by house prices in the long run

2.1.5 Demographic factors

Demographic characteristics, which reflect a population's makeup (e.g., population growth, age, and migration patterns), have an impact on both total housing costs and the sorts of houses in demand In the demographics frameworks, age structure

is most closely related to housing consumption Modigliani began researching the correlation between age and consumption in 1954 and introduced the Life Cycle Theory

G Carliner (1973) suggested that the salary elasticity of housing demand varied with

age, resulting in the differences in consumption of houses Meanwhile, R Green and P

H Hendershott (1996) did some calculation on the partial effect of age on housing demand and concluded that when all else was held constant, housing demand tended to

be flat or slightly rising with age More scholars, including McFadden (1994), Ermisch (1996), Holly and Jones (1997), and Myers and Ryu (2008), all discovered that age structure had a significant effect on housing demand, despite using different estimation methods

2.1.6 Construction cost

According to Adam and Roland (2009), construction costs include the cost of construction materials as well as the cost of labor Mayer and Somerville (2000) discovered that housing supply elasticity is inversely associated with increases in real construction costs In particular, the higher financial cost of construction will reduce construction and housing stock, resulting in higher rents and house prices Karol (2009) had been researching the construction cost transmission mechanism in the Swiss housing market As a result, he claimed that rising construction costs will cause house prices to rise, revealing property developers’ strong leverage in passing on construction costs to buyers Furthermore, changes in house prices will impact construction activities in the construction sector, particularly the profits of construction projects (Jacobsen et al., 2005) In general, construction costs are a flexible factor that has a positive impact on house prices

2.1.7 Housing stock

Housing stock is considered to have a significant impact on housing prices In six nations (the United States, the United Kingdom, Canada, Ireland, the Netherlands, and Australia), Sutton (2002) found that stock prices explain a major portion of house price changes Besides, another study by the Bank for International Settlements (2003) discovered that property prices follow the stock market with a 2-3 year lag for a large

set of countries The extent to which the link between equity and house prices indicates

a causal relationship is an important question Alternatively, because of the stock market's forward-looking tendency, equity prices may just be an excellent leading indicator Meanwhile, Peter Abelson, Roselyne Joyeux, George Milunovich and Demi Chung (2005) have concluded housing supply's inelastic character is undoubtedly one of the reasons why housing stock is rarely considered in short-run home price models Another factor could be that measuring housing supply on a quarterly basis is challenging However, leaving housing stock out of explanatory models could be a big mistake They discover that housing stock per capita has a significant long-run impact

on home prices and, as a result, on the short-run model

Table 2 1 Selected empirical studies on the determinants of house prices Authors and country | Elasticity of real house prices Methodology,

(2002) after 3 | weaker years

years for longer rates

8 transition GDP -0,002 to | Real wages- | Panel DOLS economies of Central | per -0,015 positively methodologies 1995- and Eastern European | capita correlated 2005

& 19 OECD 0,4 to with real

countries Egert, 0,6 house prices

Balazs; Mihaljek, after 10 In Central and Eastern Dubravko (2007) years Europe, the

development of housing markets and housing financing institutions appears to have a significant influence on real house price dynamics

15 OECD countries log real log real DOLS Estimation

interest money supply | Model IL rate -1,17 | (-0,89 to 1,32)

log construction cost (-0,47 to 1,77)

18 countries 0,5 to -0,5 to - Housing Dynamic factor model

1,1 1,0 short- | affordability | 1980-2004Q1 term rate | (t-1) -0,1

Terrones and Otrok House price Real estate prices (2004) (t-1) 0,5 exhibit strong

persistence, long-run Real credit 0,1 reversion to

fundamentals, and Population

growth 1,8 economic basic

dependency Bank crisis -

24 Since the mid-1990s,

real housing prices have been substantially pro- cyclical, with the average connection with output (consumption) falling

2.2 VAR literature review

To measure the effect of monetary policy on housing prices, many previous studies have examined the time lags of the variables in the Vector autoregression (VAR) model The international V AR literature on this issue is extensive, and results vary across different studies In a structural VAR, Assenmacher-Wesche and Gerlach (2008) employ panel data from 18 OECD countries and conclude that monetary policy shocks have a minor impact on housing prices and credit However, they highlight the uncertainty and wide variation across countries in their estimates In a comparable panel data analysis, Carstensen et al (2009) employ other variables and different estimation and

identification approach In contrast to Assenmacher-Wesche and Gerlach (2008), they show that monetary policy has a significant influence on housing prices, but they also emphasize the cross-country variability

For 10 OECD countries, Jager and Voigtlinder (2006) employ a structural VAR framework to compare real house price impulse reactions to monetary policy shocks According to their result, reaction of real house prices two years after a monetary policy shock hits the economy is stronger in countries where variable rate contracts predominate (UK, Spain, Finland and Australia), and weaker in countries where fixed rate contracts predominate (Germany, France and Japan) Tsatsaronis and Zhu (2004) use a VAR model that includes inflation-adjusted house prices, GDP growth, CPI, the real short- term interest rate, the term spread between a government bond with a long maturity and the short-term interest rate, and the growth rate of inflation-adjusted bank credit They argue that inflation is a significant determinant of real house prices, and that household income has limited explanatory power

CHAPTER 3: METHODOLOGY AND DATA

3.1 Data collection and specification

The data was collected from OCED Short-term interest rate and real house price index were collected from OECD database: https://data.oecd.org/ GDP deflator and GDP per capita were collected from https://stats.oecd.org/ In our scope of research,

we collected the data sets of mentioned parameters in France The time period of our subject is 20 years from 2002 to 2021, assessed quarterly (2002q1-2021q4) The total number of observations is 80 (quarters)

Real House Price Index (HPI): The real house price index is given by the ratio

of the nominal house price index to the consumers’ expenditure deflator in each country from the OECD national accounts database This index is seasonally adjusted Short-term interest rates (STIR): Short-term interest rates are the rates at which short-term borrowings are effected between financial institutions or the rate at which short-term government paper is issued or traded in the market Short-term interest rates are generally averages of daily rates, measured as a percentage Short-term interest rates are based on three-month money market rates where available

GDP per capita: The GDP per capita is measured in US dollars (the OECD reference year of 2015) This data is seasonally adjusted

GDP deflator (GDPd): The GDP deflator is measured as index (the OECD reference year 2015 as 100) with the ratio of nominal and real GDP - expenditure approach This index is also seasonally adjusted