Previous research into computer-assisted methods of residential property valuation has concentrated upon statistical techniques i.e., multiple regression analysis. Inquiries made of all leading academic and professional institutions in the United Kingdom, the United States of America, Australia and New Zealand involved in property valuation, indicate that this project is unique in current residential valuation research. The. approach is based upon a study of the working practises of a professional mortgage valuer, his "expertise" and techniques utilised in the completion of mortgage valuations. A model of the valuation procedure has been developed and exposed to the critical evaluation of other valuers. This model has been implemented as a demonstration "expert system". A critical evaluation of the suitability of the different software, knowledge elicitation and knowledge representation techniques for valuation work has been carried out and an assessment of the nature and use of uncertainty within the domain of mortgage valuation made. The current methodology effectively demonstrates the knowledge-based concept of a separation of comparable property data, and the procedures used to manipulate that data, i.e. the valuer's use of comparable s. This enables the demonstration system to operate with few and "imperfect" comparables. Additionally the methodology is not time-related, the demonstration system selects comparable information from the same time period as the required valuation. These features are clearly an advance over the regression studies noted above which require complete data in large quantities over a restricted time period. Currently the integration of knowledge-based and conventional data processing software is in its infancy and this is reflected in the limited nature of the demonstration system. In conclusion the project has developed a wholly original approach to the problem of computer-assisted residential property valuation, contributing significantly to the available literature in the comparative method of valuation, computer-assisted valuation techniques, and the identification of uncertainty within a domain of expertise.

The aim of this paper is to study the effect of interest rates on house price changes in Sweden for the case of one and two dwelling buildings. Basically, three procedures were used for analysis. First, correlation analysis was used to investigate and test if there has been any relationship between interest rates and house price in Sweden in the past two decades. Second, multiple regressions analysis with consideration of hetroskedasticity autocorrelation or HAC (newey-west standard) errors was applied to test the impact of changes of interest rates on house price. Finally, distributed lag model was applied to examine the impact of interest on house price through time. The result shows that there is strong inverse relationship between interest rates (governmental bond rates, mortgage bond rates, lending rates and repo rates) and housing price index. The regression coefficients show that the decrease in the interest rate is followed by corresponding increase in the housing price index for all the given interest rates. The other finding is that more than 92 percent variation in the housing price index is explained by changes in interest rates, changes in net house hold disposable income, inflation rate and supply. The result also shows the lag effects of changes of interest rates on housing price. The major implication of this study is that fluctuations in interest affect homebuyers, home sellers, household incomes and investors. The study also suggests that further detail investigation on house price dynamics is crucial for monetary policy.

<p>This dissertation presents the construction procedure of &ldquo;high-frequency&rdquo; daily measure of changes in housing valuations, and analyzes its return dynamics, as well as investigates its relationship to capital markets. The dissertation consists of three chapters. The first chapter introduces the house price index methodologies and housing transaction data, and reviews the related literature. The second chapter shows the construction and modeling of daily house price indexes and highlights the informational advantage of the daily indexes. The final chapter provides detailed empirical and theoretical investigations of housing index return volatilities. </p><p>Chapter 2 discusses the relationship of the housing market with the other markets, such as consumer good market and financial markets. Different housing price indexes and their construction methodologies are introduced, with emphases on the repeat sales model and S&P/Case Shiller Home Price Index. A detailed description of the housing transaction data I use in the dissertation is also provided in this chapter.</p><p>Chapter 3 is co-authored with Professor Tim Bollerslev and Professor Andrew Patton. We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity. The correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are commensurate with existing empirical evidence for existing monthly and quarterly house price series. Timely and accurate measures of house prices are important in a variety of applications, and are particularly valuable during times of turbulence, such as the recent housing crisis. To quantify the informational advantage of our daily index, we show that a relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data.</p><p>Chapter 4 investigates the properties of housing index return volatilities. Similar to stock market volatility, housing volatilities are found to respond asymmetrically to negative and positive returns. A direct test of volatility on changes in loan-to-value ratio suggests that the observed volatility asymmetry does not stem from changes in degree of housing financial leverage, but could result from the risk premium carried by housing volatility, which is supported by a consumption-based asset pricing model with housing. Moreover, housing and stock volatilities are found to be positively correlated from a set of predictive regressions based on realized variances of housing and stock markets, in which higher (lower) volatility in one market will be followed by higher (lower) volatility in the other. Finally, housing and stock cross-sectional return dispersions are shown to contain useful information in predicting both within-market and cross-market realized volatilities.</p> / Dissertation

Thesis advisor: Fabio Ghironi / This dissertation consists of three essays on macroeconomics with frictions and uncertainty shocks. The first essay is "Collateral Constrained Workers' Unemployment". Financial market and labor market are closely interconnected each other in the sense that unemployed workers have difficulty not only in borrowing new loan but also in repaying outstanding loan. In addition, if unemployment entails loss from default and no new loan, credit constrained workers will accept lower wage to avoid the loss from losing job. In this paper, we try to investigate the role of the interaction between financial market and labor market over the business cycle. To do that, we assume credit constrained workers can borrow against their houses and repay outstanding loans only when they are employed. We also introduce labor search and matching framework into our model to consider unemployment and wage bargaining process explicitly. With this setup, we find that adverse housing preference shock leads to substantial negative impact on labor market by reducing the benefit from maintaining job. As a result, high unemployment significantly amplifies the business cycle by reducing supply of loan and increasing default. This result would be helpful to understand recent "Great Recession" which was originated from the collapse of housing market and accompanied by high unemployment and default rate. The second essay is "International Financial Business Cycles". Recent international macroeconomics literature on global imbalances explains the U.S. persistent current account deficit and emerging countries' surplus, i.e., the U.S. is the borrower. Little research has been done on the banking-sector level, where U.S. banks are lenders to banks in emerging countries. We build a two country framework where banks are explicitly modeled to investigate how lending in the banking sector can affect the international macroeconomy during the recent crisis. In steady state, banks in the developing country borrows from the U.S. banks. When the borrowers in the U.S. pay back less than contractually agreed and damage the balance sheet of the U.S. banks, with the presence of bank capital requirement constraint, U.S. banks raise lending rates and decrease the loans made to U.S. borrowers as well as banks in the developing country. The results are a sharp increase in the lending spread, a reduction in output and a depreciation in the real exchange rate of the developing country. They are the experience of many emerging Asian markets following the U.S. financial crisis starting in late 2007. Another feature of our model captures an empirical fact, documented by Devereux and Yetman (2010), that across different economies, countries with lower financial rating can suffer more when the lending country deleverages. The third essay is "Uncertainty, Collateral Constrained Borrowers, and Business Cycle". Standard RBC model fails to generate the co-movement of key macro variables under uncertainty shock because precautionary saving motive decreases consumption but increases investment and labor. To fill this gap, we build a DSGE model with collateral constrained borrowers who can borrow against housing and capital. In the model with modest risk aversion, we can generate the desired co-movement of key macro variables under uncertainty shock and the co-movement comes from the collateral constraint channel through drop in housing price. Under uncertainty shock, highly indebted borrowers sell collaterals to avoid uncertainty in future consumption. As a result, housing price goes down and it makes credit crunch to borrowers through collateral constraint channel. The negative effect of uncertainty shock is strengthened in the economy with higher indebted borrowers. / Thesis (PhD) — Boston College, 2012. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

<p>This is an empirical study which goal is to determine what causes changes in housing prices. It is done by using data for Stockholm and Sydney to create a model to forecast the change of house prices in the two cities. The findings suggest that the main determinants are nominal interest, household income, and the supply of new dwellings.</p><p>This is in line with previous studies. It is also investigated whether the use of financial indicators such as the development of the stock market has an impact on the house prices.</p><p>The findings regarding the implication of the financial indicators are dubious. Lastly, an investigation is made to see whether the so-called “ripple effect” can be applied to an international level. The inclusion of the ripple effect seems to be positive to the forecasting models used in this paper.</p>

This is an empirical study which goal is to determine what causes changes in housing prices. It is done by using data for Stockholm and Sydney to create a model to forecast the change of house prices in the two cities. The findings suggest that the main determinants are nominal interest, household income, and the supply of new dwellings. This is in line with previous studies. It is also investigated whether the use of financial indicators such as the development of the stock market has an impact on the house prices. The findings regarding the implication of the financial indicators are dubious. Lastly, an investigation is made to see whether the so-called “ripple effect” can be applied to an international level. The inclusion of the ripple effect seems to be positive to the forecasting models used in this paper.

The purpose of the thesis was to study price differentials of housing in and outside of Swedish cities. When doing so, the average price of detached houses in every Swedish municipality and city was taken. The prices were based on the purchasing sum (köpeskillinen) while the investigated time period was 1995 and 2005. To separate between the different areas in Sweden, the country itself was divided into two separate regions; south, and north. South was used twice, once with the three major city areas (Stockholm, Göteborg and Malmö/Lund) included and once when they were not. Within each region two groups of locations could be differentiated; economic centres (Stockholm as an example) and sub-municipalities (Danderyd as an example). Economic centers represented “in cities” and sub-municipalities “outside of cities”. In addition to the main purpose, we also wanted to examine what variables that are affecting the price of housing. Therefore; according to our theoretical background, income, working opportunities and availability of teachers were the important factors. The empirical analysis signified that there is a clear average price differential between economic centers and sub-municipalities in all three regions. Detached houses in economic centers have become more expensive relative to sub-municipalities. The largest difference can be observed in the three major city areas, where the most extreme price changes have occurred. Consequently, it can be said that working opportunities had the foremost effect on house prices in the majority of our research areas. It was also found that income had a significant influence at several locations. Teachers per 100 students had on the other hand little or no effect at all on house prices. Moreover, where it was significant it affected houses prices negatively.

After a court imposed desegregation plan ended in 1996, the Baton Rouge, Louisiana school district created neighborhood attendance zones for its schools, followed by a series of attendance zone changes. We use data from 1994 to 2002 to examine the impact of changes in school characteristics on simultaneous determination of house prices and liquidity in the market. A simultaneous equations model of sales price and tine-on-market is adopted that extends the hedonic price model by controlling for localized neighborhood market conditions. Our empirical results show that improving and declining school performance can have asymmetric capitalization effects. Further, as indicated by the search-market model, liquidity absorbs part of the capitalization of school quality; for example, declining school performance prolongs houses’ marketing time.

<p>The purpose of the thesis was to study price differentials of housing in and outside of Swedish cities. When doing so, the average price of detached houses in every Swedish municipality and city was taken. The prices were based on the purchasing sum (köpeskillinen) while the investigated time period was 1995 and 2005. To separate between the different areas in Sweden, the country itself was divided into two separate regions; south, and north. South was used twice, once with the three major city areas (Stockholm, Göteborg and Malmö/Lund) included and once when they were not. Within each region two groups of locations could be differentiated; economic centres (Stockholm as an example) and sub-municipalities (Danderyd as an example). Economic centers represented “in cities” and sub-municipalities “outside of cities”. In addition to the main purpose, we also wanted to examine what variables that are affecting the price of housing. Therefore; according to our theoretical background, income, working opportunities and availability of teachers were the important factors.</p><p>The empirical analysis signified that there is a clear average price differential between economic centers and sub-municipalities in all three regions. Detached houses in economic centers have become more expensive relative to sub-municipalities. The largest difference can be observed in the three major city areas, where the most extreme price changes have occurred. Consequently, it can be said that working opportunities had the foremost effect on house prices in the majority of our research areas. It was also found that income had a significant influence at several locations. Teachers per 100 students had on the other hand little or no effect at all on house prices. Moreover, where it was significant it affected houses prices negatively.</p>

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