published_or_final_version / abstract / toc / Real Estate and Construction / Doctoral / Doctor of Philosophy

This dissertation consists of three papers that explore the intersection between geography and housing markets. The research examines both how geographic context shapes housing prices and how house prices influence geography through household location decisions.The first paper explores the spatial structure of housing prices within Tucson, Arizona. Hedonic house-price studies typically assume that housing attribute prices are constant over space. The research tests this assumption and compares two methods of incorporating spatial-varying parameters into house-price models: geographically weighted regression and the spatial expansion method. The results provide evidence that housing attribute prices do indeed vary with geographic context and suggest that more reliable parameter estimates and better house-price estimation accuracy can be achieved through the use of these techniques.The second paper builds upon the first by examining how more realistic conceptions of housing market spatial structure influence the hedonic price estimates of location-specific externalities. The empirical analysis examines how two key spatial effects, spatial dependence and spatial heterogeneity, impact the marginal price estimates for proximity to the Rillito River, within Tucson, Arizona. Both spatial effects are found to influence the resulting estimates, but spatial heterogeneity is of greater practical importance as the price estimates vary widely with geographic context. This research highlights the importance of considering both spatial effects in hedonic externality valuations.The final paper explores how housing prices influence interregional migration patterns, and more specifically, how their influence varies with both stage in the life course and educational attainment. The research models metropolitan migration within the United States during the period 1995 to 2000. The results indicate that housing prices play an important role in driving regional demographic change, as their influence varies with both demographic characteristics. High housing prices deter individuals in their late twenties and early thirties, but their influence wanes during middle age. House prices become more important as individuals near retirement. The results also provide evidence that college graduates respond more to house price differentials than do persons with lower levels of educational attainment.

Many countries such as Australia, Ireland, Netherlands, United Kingdom (UK), Spain, United States of America (USA) and South Africa (SA) among others have experienced an increase in housing prices, since the late 1990s. In SA, the abrupt increase in residential property prices, particularly during the period 1999 to 2007, resulted in an improvement in the level of households’ net wealth position. Empirical investigations, mainly from developed countries, provide evidence indicating that a house price increase has a significant impact on the households’ wealth, and thus house price gains increase housing collateral for homeowners which make it possible for them to take out equity in the form of refinancing or selling of the house to finance consumption. With the above in mind, this study investigates the relationship between aggregate expenditure on consumption by households and residential house prices in South Africa. Following the permanent-income/lifecycle hypothesis (PI-LCH), this study applies the vector error model (VECM) into the 1980:Q1 to 2007:Q4 quarterly data sample. The overall finding of the study indicates there is indeed a long-run positive relationship between housing prices and consumption in South Africa. / Thesis (M.Com.)-University of KwaZulu-Natal, Westville, 2010.

Bostadsmarknaden är en av de största tillgångsmarknaderna i ett land varpå förändringar i bostadspriserna får långt gångna konsekvenser för det enskilda hushållet, det finansiella systemet och samhällsekonomin i stort. Flertalet tidigare studier har analyserat den svenska bostadsmarknaden utifrån ett storstadsperspektiv alternativt jämfört Sveriges bostadsmarknad mot andra länder. Vi har identifierat att studiet kring vad som bestämmer prisnivån på regionala bostadsmarknader i Sverige är tämligen oexploaterat varför avsikten med den här studien är att analysera bestämningsfaktorer till de svenska bostadspriserna på länsnivå. Sålunda är ett bidragande mål med denna studie att tillföra en bättre förståelse för dynamiken på den svenska bostadsmarknaden. I studien använder vi multipel regression där vi bearbetar paneldata med en Fixed Effect Model. Ett flertal förtester har gjorts för att få fram den mest tillförlitliga modellen i vilken vi skattat bostadspriserna utifrån teoretiskt belagda förklaringsvariabler. De slutsatser vi har dragit är att disponibel inkomst, befolkningstäthet och sysselsättningsgrad kan förklara bostadspriserna på länsnivå med en procents signifikansnivå. Skillnaden i bostadspriserna mellan länen har relativt sett ökat över tidsperioden för studien. Avslutningsvis diskuteras uppvisade avvikelser mellan de verkliga bostadspriserna och de skattade bostadspriserna vilka kan förklaras av att bostadsmarknaden är känslostyrd med inslag av spekulationer. / The housing market is one of the greatest assets markets in a given country. Therefore, changes in housing prices have a big impact on the single household, the financial system and the economic system as a whole. Due to the housing markets vital role in the society, many scientific studies have been done with the purpose of enlighten and discover the dynamics of the Swedish housing market. The focuses in these earlier studies have more than often taken a metropolitan perspective or compared the Swedish housing market with other countries. However, this study divides the Swedish housing market into regional county level with the purpose of analyzing determinants of housing prices due to county specific variables. By analyzing the housing prices due to county specific factors a contributing goal with this study is to deepen the understanding about the dynamics in the Swedish housing market. In this study we have used multiple regressions in order to work with panel data. The Fixed Effect Model fitted our purpose well which is why that kind of model was used in order to estimate the housing prices for every single Swedish county. The conclusions drawn in this study are that disposable income, people density and employment rate are all statistically significant on one percent level in order to explain the housing price at state level. We have also discovered that, during the observed period, the relative differences in housing prices between the different states have increased. Finally, the differences found between the real housing prices and the estimated housing prices, can be explained by the assumption that the housing market is driven by emotions and speculations.

Developing timely and reliable house price indices is of interest worldwide, because these measures influence consumer behaviour, inflation targeting, and spot and futures markets. Several techniques for constructing a constant quality price index are available in the literature, but these methods are difficult to apply in localities where market transaction data is limited. Since house price movements are a local phenomena, improving the timeliness of a quality controlled price index at local housing market levels in small countries like New Zealand is a challenge. This thesis comprises three essays that focused on improving the timeliness of reported house price indices at the local market levels. The timeliness issue examined in this thesis has not previously been rigorously investigated and this makes the results of this thesis both important and unique for the benefit of both academic research and practical application. Essay One reviews the sale price appraisal ratio (SPAR) method, which has been applied since the 1960s for producing local house price indices at a semi-annual and quarterly basis in New Zealand. Utilizing a variety of statistical tests and comparing this index with the repeat sales and median price index result in the study highlighting the potential of, as well as the problems associated with, a price index produced by the SPAR method at a monthly level. In the following two essays, monthly price indices are tested using empirical real estate research methods in order to examine their usefulness in exploring the research questions as well as revealing the statistical differences between them. Essay Two studies the relationship between sale price and trading volume, and the ripple effect of local house price comovements. The results show that the trading volume generally leads the sale price in the long-run and the ripple effect is most likely constrained within regions. In Essay Two, the monthly SPAR index produces similar statistical results to those estimated by the repeat sales index for large cities. Essay Three is a study on the market efficiency of housing markets. It is found the local housing market is neither weak-form nor semi-strong form efficient. Local house price movements are strongly correlated and are mean reverting towards their long-run equilibrium. It is further concluded that monthly price indices for small cities are problematic due to the problem of small sample size. Overall, the findings in this thesis show monthly house price indices can be generated by using the SPAR method at local market levels. However, this potential is limited to large cities. Further research can focus on improving the quality of monthly price indices for large cities.

Sverige är ett av de länderna som har haft en väldigt kraftig uppgång på bostadspriserna de senaste åren enligt Statistiska centralbyråns (SCB) siffror. Utvecklingen har inte endast påverkat huvudstaden utan även andra städer i landet. Faktorerna bakom prisuppgången må vara många men studiens syfte är att uppskatta sambandet mellan bestämningsfaktorerna: befolkningstäthet, förvärvsinkomst, nyproducerade bostadsrätter i förhållande till folkmängd, bolåneränta samt arbetslöshet med bostadsrättspriserna. Fokus kommer ligga på storstadsregionerna Stor-Stockholm, Stor-Göteborg och Stor-Malmö mellan år 2008-2016. Bostadspriserna analyseras ur ett grundläggande nationalekonomiskt perspektiv och undersökningen har formats med hänsyn till det statistiska materialet som funnits åtkomligt, för att testas med hjälp av paneldata där fixed effect har innefattas. Studien omfattar 45 kommuner med en tidsperiod på 9 år och mängden observationer uppgår till 405, där datamaterialet bedöms vara komplett utan några bortfall under tidsperioden, det vill säga balanserad. Studiens resultat visar ett positivt samband mellan befolkningstäthet och bostadsrättspriserna, en ökning med en invånare per ökar bostadspriserna med 15,8 kr per kvm, såvida de andra variablerna hålls konstanta (modell 7). Regressionresultatet visar på att befolkningstätheten är den faktor som har den främsta påverkan på bostadsrättspriserna i jämförelse med studiens övriga faktorer. Resultatet påvisar även ett positivt samband mellan förvärvsinkomst och bostadspriserna samt ett negativt samband mellan faktorerna arbetslöshet och ränta på bostadsrättspriserna i samtliga modeller. Däremot visar nyproduktionen ett oväntat resultat. Studien skall bland annat bidra till en ökad förståelse för sambandet mellan de utvalda bestämningsfaktorerna och dess inverkan på bostadspriserna. Vidare är studiens källor tillförlitliga och har en stark validitet som ger en ökad förståelse för sambandet. / Sweden is one of the countries that has had a very strong increase in the housing prices during the recent years according to SCB’s calculations. The development has not only been seen in Stockholm but it has also affected the smaller cities as well. The factors behind the massive price increases may be many, but the purpose of the essay has been to estimate the relationship between the factors: population density, income, new constructed condominiums in relation to the population, interest rate and unemployment have on condominiums rates. The main focus will lay on the following big cities, Stockholm, Gothenburg and Malmö from the years of 2008-2016. The housing prices will be analyzed from an economic perspective. Further, the study has been formed by the statistical material that has been accessible and then been tested using panel data that includes fixed effect. The study encompasses 45 municipalities with a time period of 9 years (2008-2016) which comprises to 405 observations, where the data is strongly balanced, meaning that the database is complete. The result of the essay shows a positive correlation between population density and housing prices, an increase of one inhabitant per increases housing prices by 15.8 kr per square meter, if all the other variables are hold constant (model 7). The regression results indicate that population density is the factor that has the main impact on housing prices in comparison to all other factors. The results also shows a positive correlation between income and housing prices, as well as a negative correlation between the factors unemployment and the interest rate on housing prices in all models. On the other hand, new production shows an unexpected result. Furthermore, the essay considered to contribute to a better understanding of the relationship between the selected factors and its impact on the housing prices in Sweden's three largest cities, Stockholm, Gothenburg and Malmö during the period 2008-2016.

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida. / Series: Working Papers in Regional Science

The housing prices in Prague had risen by more than 63% between 2002 and 2008. However since August 2008, when they reached their peak, they have been almost constantly falling. Most of the publications regarding housing prices are taking into account Prague as one of the Czech regions. On the contrary, this work analyzes housing prices in Prague and its 22 districts. In first part of this work the indicators of rent-to-price ratio, price-to-income ratio and new building development factors are considered to explain possible future development of housing prices. This is seemed to continue falling during the year 2011. The second analytical part tries to explain the housing prices according to specific locality features and macroeconomic aspects. The panel regression is used and it reveals that the most significant attributes are the macroeconomic features.

The nation’s economy depends heavily on mobility of goods and people. As communities look to improve mobility, many options can be considered, including roadway improvements, congestion-pricing options such as dynamic tolling and toll lanes, and public transit. Investment in public transit services can come in the form of increased and enhanced bus services, including bus rapid transit (BRT), as well as rail transit investments. As BRT continues to grow in popularity in the United States, a better understanding of the mode’s impacts on land uses and economic development is needed. One method of assessing the mode’s impacts is by examining the market value of properties with access to BRT stations. Based on land-rent theory, it is hypothesized that people will be willing to pay a premium for convenient and reliable access via BRT to the central business district (CBD) or other locations with employment, educational, recreational, and shopping opportunities. Very little research has been conducted on BRT as it operates in the present day in the United States. For this work, the hypothesis is that the BRT stations have a positive impact on the market value of residential properties. To test this hypothesis, hedonic price regression models are used to estimate the impact of access to BRT stations on the sale prices of surrounding single-family homes using a case study of the HealthLine BRT system in Cleveland, Ohio that began operating in 2008. Three time periods were examined: 2004, the year construction began; 2008–2009, after the HealthLine BRT service began operation; and 2010–2011, the latest year for which sales data are available. Despite a documented decline in median sale prices of single-family homes in the city of Cleveland from 2005 to 2011, overall results of the analysis were mixed. Although it was prior to the opening of the BRT system, the 2004 data did not show any impacts of the stations on surrounding home sale prices. For the 2008–2009 data, positive and statistically significant impacts were found; however, the positive impacts did not persist in the 2010–2011 data. It would likely be necessary to seek out additional years of data to fully answer the question posed by this research. It is important for decision-makers to have the most accurate and most recent information on the benefits and costs of all transportation alternatives, including BRT. The research presented herein makes a significant contribution to filling the current gap in quantitative research on the subject and provides planners, policymakers, and the transit industry with the best information possible to make sound transit investment decisions in their communities.

Sports facilities increasingly feature amongst the most expensive development projects world-wide. One such facility includes world-class stadia. Such facilities tend to commit a considerably large amount of a country's public funds to the project. This public expenditure on new stadia, and the required public taxation, may be economically justified if the benefits from the new stadia outweigh the costs. 15 May 2004 saw South Africa winning the bid to host the FIFA 2010 Soccer World Cup tournament. This mega-event was played in 10 stadia across nine chosen host cities. Five of these stadia were newly constructed, while the other five needed upgrading. Both South Africa's national government and local governments of host cities bore the expenses of the new stadia construction and the upgrading to the existing stadia. This amounted to a total public expenditure of R13.5295 billion on the stadia alone. The Nelson Mandela Bay Stadium on the banks of the North End Lake in Port Elizabeth was amongst the five newly constructed stadia costing R1.7 billion. Many international studies have been conducted to assess the impact of new stadia on the economies of host cities. One particular aspect which has received a lot of attention as far as empirical research is concerned is the impact of stadia on residential property prices (Carlino & Couslon, 2004; Davies, 2005; Tu, 2005; Coates & Humphreys, 2006; Ahlfeldt & Maennig, 2007, 2010; Dehring, Depken & Ward, 2007; Feng & Humphreys, 2008, 2012; Kavetsos, 2010; Ahlfeldt, Maennig & Scholz, 2010; Kiel, Matheson & Sullivan, 2010; Ahlfeldt & Kavetsos, 2011; Coates & Matheson, 2011). The majority of the studies conducted have indicated that the presence of a new stadium in an area has a significantly positive effect on surrounding house values that decays with distance from the facility. As no study has yet been done in South Africa to investigate the impact of the announcement of the construction of new stadia on nearby residential property values, this study examines, by means of the hedonic pricing model, the effect of the announcement to construct the Nelson Mandela Bay Stadium on the banks of North End Lake on adjacent residential property values. The study period for this study was 2004 - 2006. This time period captured the stadium announcement effect. The residential properties in North End that were traded at least once during the period 2004 to 2006 made up the target population. According to the South African Property Transfer Guide (SAPTG), a total of 417 property transactions (excluding repeat sales) took place over the study period (2004 - 2006). The 417 transactions were deemed to be the size of the target population and a list of 100 property transactions were used as the sampling frame. As the study period was from 2004 - 2006, it was necessary to adjust the market prices to constant 2006 prices. For this purpose, data from the Port Elizabeth and Uitenhage section of the ABSA house price indices were used so as to eliminate any inflationary effects on the property values over the study period. The results of the study revealed that the stadium has a statistically significant positive effect on adjacent residential properties situated within a 1 200 metres radius from the stadium. The average owner of a residential property in North End would be willing-to-pay between R10 7898 and R11 704.6 to be situated 435 metres closer to the stadium.