Econometric Analyses of International Housing Markets

Rita Yi Man Li, Kwong Wing Chau, Kwong Chau

• Publisher: Routledge
• Year: 2016
• Print ISBN:
  9781138821934
• eBook ISBN:
  9781317587910
• Topic: Business
• Subtopic: Real Estate
• Index: Business

This book explores how econometric modelling can be used to provide valuable insight into international housing markets. Initially describing the role of econometrics modelling in real estate market research and how it has developed in recent years, the book goes on to compare and contrast the impact of various macroeconomic factors on developed and developing housing markets. Explaining the similarities and differences in the impact of financial crises on housing markets around the world, the author's econometric analysis of housing markets across the world provides a broad and nuanced perspective on the impact of both international financial markets and local macro economy on housing markets. With discussion of countries such as China, Germany, UK, US and South Africa, the lessons learned will be of interest to scholars of Real Estate economics around the world.

1 Introduction

Rita Yi Man Li and Kwong Wing Chau

Housing not only provides shelter for human habitation, it also plays an important role in the macroeconomy. As home purchase often needs the input of workmen from various sectors, homebuyers not only pay for the housing unit alone but also provide jobs and business opportunities for various sectors. For example, lawyers provide legal advice and services, bankers provide mortgage services, while construction workers and designers help decorate houses. Hence, the flourishing and withering of the housing market does not solely affect the real estate sector, but many other sectors which provide numerous services and products.

Previous research suggests that housing price trends were affected by many different factors. For example, Lee and Reed (2014)’s research suggests that the First Home Owner Grant scheme stabilized the housing price in Australia. In Beijing, the three market fundamentals – namely, the population, the housing vacancy area and urban disposable income – explained 60% of the observed housing prices changes (Li and Chand 2015). In Hong Kong, capital market variables explain 75% of the changes in housing prices (Chau et al. 2001a). Furthermore, residential units with lucky floor numbers are more expensive during property booms but not in property slumps (Chau et al. 2001b). Another piece of research found that an increase of one millimeter’s annual rainfall led to a decrease in housing prices from £4 to £14 per square meter. Home purchasers living in regions with higher rainfall were willing to pay less for an extra millimeter of rain on average (Li 2009).

Housing markets are also affected by economic and financial conditions. Various macroeconomic factors affect real estate investment yields and home purchasers’ returns. When the economy is bad, unemployment is high, workers scramble for jobs and workers’ incomes fall; capability and confidence of home purchase drops substantially, leading to a potent negative drag on the number of homebuyers. Hence, in times of global financial crisis – such as, the Asian Financial Crisis in 1997 and the Global Subprime Financial Crisis – not only did income across the board decline, causing local housing prices to drop substantially, but housing prices in other places also recorded a huge drop. In India, while the share price index, non-food bank credit and foreign direct investment positively affected housing prices, the rate of inflation and market capitalization negatively affected housing prices (Mallick and Mahalik 2015). Capital inflows which were too large to absorb and distorted policy choices have always been regarded as a major factor, which led to the previous real estate sector’s collapse (Cheong et al. 2014). In Korea, for example, it was found that the impact of school proximity and scenic views dropped after the financial crisis (Kim et al. 2015).

As housing prices are quite high in many places, many of us do not have sufficient funds and rely on the mortgage provided by financial institutions such as banks. Many homeowners need to pay interest to these companies periodically. Beyond doubt, interest rates always play an important role in our global housing market. In times of high interest rates, the costs of borrowing are high and the number of borrowers decreases accordingly (Li 2015a). Therefore, many countries and cities increase interest rates to control the skyrocketing housing market, or lower rates substantially to prevent the collapse of housing prices.

Similar to many other products in the market, various characteristics of housing affect the changes of property prices. The nature of the property, quality of tenant, liquidity, duration of the lease, location and nominal yields are important factors which also affect property market yields and demand for home-ownership (Karlsson, 2003). In some highly populated cities, such as Hong Kong, correlations between the business cycle and the housing price cycle are very high. Some researchers have even commented that the “housing cycle is the business cycle” (Leamer 2007).

To study the implications of various macroeconomic factors on the housing market, much research has applied various econometrics models to the factors that drive the ups and downs of demand, supply and prices in various housing markets around the world; models such as multivariate time series models, State Space models, Vector Error Correction models, and so on. Despite the fact that econometrics techniques have been applied in housing market analysis, academic research which has applied econometric models to international housing markets is scarce. One of the objectives of this book, therefore, is to provide information on how to apply econometric techniques to different housing markets around the globe.

Is housing market analysis a local or an international issue? Due to the immobile nature of housing, housing research has traditionally been local. The provision of local facilities – proximity to the public transportation network, shopping malls, schools, libraries, swimming pools – is often identified as an important factor which drives or reduces the demand for housing and housing prices. Likewise, local environmental factors such as water seepage (Li and Li 2011, Li 2012), air quality (Chau et al., 2005 and Chau et al., 2011), and noise level (Chau et al. 2005) also affect housing prices. For practitioners, local knowledge is very important in making informed housing investment decisions.

From the early 1990s, technological breakthroughs have changed the façade of technology. The development of information technologies not only changes our form of communication but also shortens the distance between different parts of the world. We know more about cities that are far away from us through the World Wide Web. Today, we can acquire information on the housing market in different cities from various real estate resources, such as websites provided by the government, real estate consultants, agents and developers through the Internet. Knowledge of the housing market is no longer local.

In the twenty-first century, local housing market information is globally available to Internet users. We can be a knowledge receiver and sharer at the same time. In addition, the popularity of social media such as Twitter, Facebook and LinkedIn has changed to norm of communications, information distribution and knowledge sharing process. The use of the smart phone even opens up another gateway of communication and knowledge sharing. Many of us now use Web 2.0 tools, such as WhatsApp, YouTube and WeChat, to communicate and share what we know (Li 2015b, Li and Poon 2013, Li and Ah Pak 2012). Despite the potential danger of information explosion and overload, it is undeniable that the Internet has tremendously reduced the transaction cost of obtaining housing market information (Springer 1996). At the same time, the availability of housing market data has made quantitative analysis of the housing market using econometric tools more viable than before. Housing researchers and analysts need to use these tools to make the best of available housing market information.

As a result of globalization, there is an increasing proportion of non-local players in the housing market, especially in markets with little restriction on capital flows. In many cities – such as, New York, London and Hong Kong – the percentage of international buyers in the housing market has been increasing. Globalization has also led to more independence of economic performance across different regions. Since the housing market is closely related to economic performance, regional housing markets today cannot be viewed as closed and independent of other markets.

In view of the above, real estate researchers, consultants and investors need to have a regional and global perspective in order to better understand the housing market, as well as the skills to analyze market data, which is now much more readily and cheaply available. Taking examples from various housing markets to illustrate the application of econometric tools, this book introduces readers to the variety of housing markets around the world. In this book we include studies of mainland China, Hong Kong, South Africa, the Czech Republic, Germany, Norway, Canada, Japan, the US and the UK.

Chapter 2 provides a brief introduction of the role of econometrics modeling in global real estate market research. It provides a summary of the mathematical background of State Space models, the HP filter, VECM, impulse response functions, Probit model, Hedonic Pricing models and Cobb–Douglas models. Studies of these techniques will be used to illustrate their application.

Chapter 3 studies various factors which affect housing entrepreneurs’ decisions in China’s housing market. Traditional economists consider the role of the entrepreneur as being the one who bears the risk. Successful entrepreneurs are also the group of elites who reap a profit in return for risk-taking and the exercise of initiative. Housing developers are no different. In many countries, developers predict the taste of potential buyers and make decisions on the basic fittings (floor and wall finishes, bathroom fittings, kitchen cupboards) so as to save the buyer the time of decorating the housing unit before they move in. Developers can also provide the fitting and finishes more cheaply due to economies of scale. In mainland China, however, some units or houses are sold without floor or wall finishes, kitchen or bathroom fittings (bare units). What is the motive behind such a choice? This chapter analyses the factors which affect housing developers’ decisions to provide fittings by looking at 1,701 first-hand housing developments in Chongqing and Hangzhou by using a dichotomous Probit model. The results show that developers build a higher proportion of bare units in mainland China when: (1) there is a shortage of supply; and (2) land costs are high, so the costs of providing fittings and finishes become relatively low.

Apart from the direct housing market, it is often thought that real estate is an important indirect housing investment, which allows small investors with limited capital to invest in the physical market. Chapter 4 sheds light on the methods that can be used to forecast real estate stock prices. Previous studies show that forecasting stock prices is difficult because stock price performance is affected by many factors, both the macro economy and the micro operation of companies. Furthermore, data is usually reported at varying frequencies. While some are reported monthly, others are released semi-annually. This chapter aims at finding the relationship among the macro economy, corporate performance and companies’ stock prices. In this chapter, we forecast the monthly property stock price using the State Space model, which has its origin in engineering. It helps us to predict the monthly stock price by using lower frequency macroeconomic data, often released yearly or half-yearly, as well as biannual micro corporate performance data.

Many finance theories were built on the assumption of rational human behavior and negligible transaction cost. They assume that asset markets are efficient: market participants maximize returns on investment by making use of all the available information that will affect an asset’s price. This means that any observed asset price reflects all available information, which renders forecasting of future asset prices impossible. Nevertheless, the high transaction costs in the housing market implies that the capitalization of price-influencing information into asset prices may be a slow process, and therefore future prices may be predictable. The 2013 Economic Nobel Prize laureate Eugene Fama suggests that asset prices are unpredictable. However, another 2013 Nobel Prizewinner in Economics, Robert Shiller, proposes that the rises and falls in asset prices were often guided by the psychology of the investors: ups and downs of the asset prices could be guessed at by studying market investors’ behavior. Given the above two diverse but contradictory points of view, we look at property market behaviors in times of peak and trough via news recorded in 2003 and 2013 in Hong Kong in Chapter 5.

Many Hong Kong Chinese have an article of faith in lucky numbers (Chau et al., 2001b). They believe that the pronunciation of four and 14 are similar to the Chinese word “dead.” The number eight resembles the idea of wealth while three means “alive,” which brings good luck and health to residents. Hence, many of the modern designers of residential estates remove all the so-called unlucky floor and room numbers. Some firemen, however, find this adversely affects their work: when somebody claims to be on the 63th floor, that may instead be on around the fortieth floor only. Chapter 6 sheds light on Tsueng Kwan O, a new town in Hong Kong with many young couples and families, to test if there is any impact of lucky and unlucky numbers on residential property prices, and indirectly on the younger generation.

Previous research suggests that positive externalities lead to higher property prices while negative externalities lead to lower property prices. Nevertheless, few studies have been conducted on the impact of better views, air and noise pollution on housing prices. Chapter 7 studies the impact of positive and negative externalities from 1994 to 2013 on property values in Amoy Garden, one of the largest properties in Hong Kong.

We shed light on the impact of better views, noise pollution and air pollution on the property values. All the Hedonic models show that residential units on lower floors sold at lower prices, though flats which faced the noisy road sold at higher prices. Furthermore, property values of flats on lower floors dropped greater than those of higher floors if the air pollution became worse. This implied that the positive impact of a view outweighed the negative impact of air and noise pollution.

As previous research showed that financial crises usually impose negative impacts on exports and unemployment rates, Chapter 8 reviews the subprime mortgage crisis problem in the US from 2007 to 2009. It was considered the worst financial crisis since the Great Depression. This chapter analyzes the impact of the subprime financial crisis before and after the financial crisis with the help of Chow tests, L1 model and Quantile regression analysis. The results showed that housing prices in Germany dropped during the subprime financial crisis as shown in Quantile regression and Chow tests, although the impact on Norway’s housing prices was insignificant during the whole period of the financial crisis.

Chapter 9 studies the interest rate exposure of the housing markets and the role of housing prices in the monetary transmission mechanism in two emerging markets, the Czech Republic and South Africa. The Granger causality test results indicated that housing price fluctuations create wealth and a balance sheet effect in both countries. The results of impulse response functions based on the Vector Error Correction models showed that the impact of wealth and the balance sheet effect are greater in South Africa, and South Africa faced a gre...

Table of contents

• Cover Page
• Econometric Analyses of International Housing Markets
• Routledge Studies in International Real Estate
• Title
• Copyright
• Contents
• 1 Introduction
• 2 Applied Econometric Models in International Housing Markets: Theories and Applications
• 3 Risk Averse Real Estate Entrepreneurs in Mainland China: A Probit Model Approach
• 4 Forecasting Real Estate Stock Prices in Hong Kong: A State Space Model Approach
• 5 Market sentiment and property prices in Hong Kong: a heteroscedasticity-and-autocorrelation-consistent approach
• 6 Superstition and Hong Kong Housing Prices: A Hedonic Pricing Approach
• 7 Negative Environmental Externalities and Housing Price: A Hedonic Model Approach
• 8 The Impact of the Subprime Financial Crisis on the German and Norwegian Real Estate Markets: L1, the Chow Test and the Quantile Regression Approach
• 9 Housing Prices and External Shocks’ Impact on South Africa and the Czech Republic’s Housing Prices: A Vector Error Correction Model and Impulse Response Functions Approach
• 10 Factors which Drive the Ups and Downs of Housing Prices in Canada: A Cobb–Douglas Approach
• 11 An Econometric Analysis on REITs Cycles in Hong Kong, Japan, the US and the UK
• 12 Conclusion: Should Mainstream Economists Neglect and Undermine Real Estate Economics? The ‘Wh’ Questions in the International Housing Markets, Macroeconomy and Econometric Models Context
• Index