The valuation of the built environment has been a traditional concern of geographers. A particular interest has been the way in which the value of locational externalities are incorporated into house prices through housing market dynamics. However, much of the previous research into this process has been of North American origin, despite the fact that house prices, and property valuations in general, have become a major part of British life. This research aims to begin to rectify this shortfall by studying the spatial dynamics of the Cardiff Housing Market. Implicit in this research is an attempt to move towards a valuation of locational externalities at the micro-scale. The research employs two distinct method of analysis. Firstly, ARC / INFO GIS is used to construct a context-sensitive GIS of the Cardiff housing market. An important aspect of this GIS is the use of Ordnance Survey's ADDRESS-POINT product to geo-reference individual properties to a resolution of 0.1 metre. Several large and complex socio-economic and property related datasets were then attached to this coverage, including house price survey data, local taxation data, and data from a Housing Condition Survey of one in five dwellings in the central area of Cardiff. This GIS is one of the most comprehensive constructed for any city, and is relatively unique in this kind of research. The second method of analysis employs the hedonic pricing technique to impute monetary values for the implicit attributes of housing. An important part of the research is an investigation into the specification of the hedonic house price function. The traditional specification is essentially aspatial, and does not take into account the spatial nature the data, and thus the spatial dynamics of the housing market that generates it. To rectify this, three different specifications of the hedonic house price function are investigated: the traditional specification, the spatial parameter drift specification and the multi-level specification. The research concludes that the multi-level specification is best at modelling the spatial heterogeneity and spatial dependence inherent in housing market data. The results from this modelling show that the valuation of locational externalities are intimately bound up with the attributes of the housing stock and the characteristics of the resident households, resulting in a complex juxtaposition of positive and negative valuations of location at the local level.

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As swedes are keen on outdoor recreation and like to spend on travels and gear for forest recreation we want to show if there is a willingness to pay for forest recreation close to home. Starting in hedonic regression we create a model using publicly available data to show how consumer preferences effect house prices. Results show that distance to forest have a small but significant impact on house prices. To conclude we state that a logarithmic model using open source data can be useful in city planning and that there is a positive effect of nearby forests on house prices.

This study examines the effects of educational submarkets -- schools and districts -- on house prices in the Tucson region. The supposition that homebuyers will pay more to live in a better school district or school attendance area is examined, with the quality of education measured by per-student expenditures and academic achievement. Traditional single-market modeling of the housing market finds that education submarkets have a small but significant effect on housing price. Further modeling, taking explicit account of the spatial nature of the housing market, suggests that in the single-market approach, education submarkets act as proxies for other neighborhood effects and variables omitted from the model. Incorporating the unique location coordinates of the properties and allowing marginal attribute- and location-effects to vary across geographic space in a trend surface approach produces more robust model results and allows the educational submarket effects to be isolated. The results suggest that school districts have a small but significant price effect even after a fluid price surface has been developed, but that intra-district variation remains. These price effects have some relationship with district quality as measured by academic achievement, but the housing market does not reward per-student expenditures. At the intra-district level, middle school quality does not appear to have a significant effect on housing price, at least in the Tucson Unified School District. However, the trend surface approach still proves to be a useful methodology for modeling small, local-scale variations. The use of polynomial expansion and spatial- attribute variable interactions is successful: problems of variable omission are diminished, spatially autocorrelated error terms are reduced and removed, effects of multicollinearity are minimized, and the effects of the educational submarkets may be examined in isolation.

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / We analyse the role of financial and macro-economic variables in the conduct of monetary policy, particularly the role played by monetary policy in the house price boom of the early 2000s. The analysis is performed in the setup of a New Keynesian open economy. We estimate a five variable Recursive Vector Autoregressive model consisting of the short term interest rate, house prices, inflation, output and the exchange rate. Quarterly data from 1994 to 2011 was inputted in Eviews (6) to run the model. We find a significant causal relationship between the short term interest rate and house prices; the impulse response results show an instant response of house prices to a shock in monetary policy. We conclude that the house price boom of the early 2000s was partially attributed to an overreaction to a shock in monetary policy. We also find evidence of exchange rate pass- through to the consumer price index as in (Mishkin, 2008).We conclude that perhaps monetary policy should take cognisance of asset price fluctuations and exchange rate volatility in determining the policy instrument

<p> </p><p>This bachelor thesis examines the credit market behaviour in the Scandinavian countries (Sweden, Finland, Denmark and Norway), post financial liberalization, during the late 1980´s and early 1990´s. The explanatory variables used to determine bank lending are the time lags of bank lending, property prices, GDP and interest rates.</p><p>The variables’ impact on bank lending is tested and displayed by using an OLS model,presented by Goodhart and Hofmann (2007), and descriptive statistics.</p><p>The rolling OLS regressions show that during times of financial liberalization, property prices had an increased effect on real bank lending in Sweden and Finland. The same investigation method supports that although positive, property prices’ effect on lending did not increase in Norway and Denmark. Even so, investigations suggest that one should be careful to assume too many similarities between the countries in the causing factors of the crises. The crises occurred roughly during the same time, and the geographical connection is obvious, however each country’s individual factors differed from each other.</p><p> </p>

The issue of the rising Claremont water rates has been a heated topic of debate over the last couple of years. This study is part of a larger body of literature that seeks to determine the extent to which a location-specific amenity or dis-amenity is capitalized into housing values, and specifically aims to examine the relationship between water rates and home prices in Claremont. To do this, it uses houses in La Verne, where water rates have not been rising as rapidly, as a control group. Specifically, the regression model looks at whether the differences in the water bills between Claremont and La Verne are associated with the differences in the house prices between Claremont and La Verne. In doing so, it also provides an estimate of the price discount faced by houses in Claremont due to the water rate.

A sharp rise in house prices became a political issue in Korea in the late 1980's. It opened a heated debate among scholars and experts on the causes of house price rises. In the debate, a common belief was that high land prices had been one of the main causes of high house prices. In fact, the proportion of land cost in new house prices has steadily increased as the latter has risen in Korea during the past two decades. This research calls into question the notion underlying that common belief that land prices exist independently of housing development. Most policy measures are sought on the basis of this notion. This research argues that land prices are an outcome of conflicts between landowners and developers in the process of housing development. In exploring the idea that the determination of land cost for housing is a result of conflicts between landowners and developers, the research came to the question of what the source of conflicts is, a question of why and how the two actors enter into conflictive relations. It was a suggestion of Marx's concept of rent that surplus profits are the material source of conflicts; the ability of developers to create higher surplus profits provides possibility of landowners to demand more payments for their land; landowners' appropriation of increasingly larger portions of surplus profits then conditions the way developers produce housing; thus both enter into a conflictive and contradictory relationship. It was thus hypothesised that the rising land cost for housing has been primarily a result of that conflicting and contradictory interaction, which is permanently operating in housing development. However, how far and in what forms the conflict affects housing development and the determination of land cost are affected by social mediation of the interaction. Thus the research, the test of the above hypothesis, comprises two parts: the identification of the material aspect of the process by which landowners and developers entered into conflicting relations resulting in increasing land prices for housing as suggested in Marx's concept of rent; and the examination of political and economic circumstances in which social relations between the two actors were conditioned to leave that material process unregulated. This hypothesis was tested with reference to the case of housing development in Seoul during the 1970's and 1980's. The empirical examination disclosed that the rising land cost for housing in Korea has been due to the conflictive nature of the relationship between landowners and developers. Developers have created large surplus profits by exploiting rapidly growing speculative demand for housing and government housing programmes relying on private development; this have provided room for landowners to raise land prices such that increasingly larger portions of new house prices have been allocated to land cost; increasing government intervention have been unsuccessful in controlling this conflictive and contradictory process and the consequent spiral rises in land cost and house prices because of its inability to break from its self-financing housing development strategy; this inability has been due to historical circumstances which, characterised by strong state and weak labour relations and the subordination of finance to industrial capital, have conditioned housing development to be driven by the private appropriation of development gains.

The research methodology comprises theoretical, empirical and evaluation stages.  The theoretical stage provides evidence that substantiates the need for the study and outlines possible ways to address spatial elements in hedonic price modelling.  The empirical stage illustrates the application of GIS and spatial statistics in the estimation of hedonic models for housing markets in Glasgow, Scotland, using 2,715 house prices for 2002 and 61 independent variables.  GIS is used in this study to construct spatial variables including detailed accessibility measures, to help detect the hedonic problems of heteroscedasticity and spatial autocorrelation, and for visualisation.  Spatial statistics are used to test formally and model explicitly the spatial autocorrelation.  The evaluation stage assesses 46 hedonic models, using OLS and spatial hedonic, for <i>a priori </i>segmentations involving the spatial, structural and nested sub-markets.  It also draws general conclusions about the importance of detailed accessibility measures and spatial statistics in sub-market modelling. This study finds that the nested sub-market modelling using a spatial hedonic approach is most effective, followed by the spatial and structural sub-markets.  The OLS sub-market modelling generally reduces spatial autocorrelation but does not eliminate it.  There is a greater incidence of spatial autocorrelation when the market size, with measured by geographical area or density of dwellings is larger.  The spatial hedonic modelling improves the performance of the individual OLS models and the three segmentation approaches, although the relative performance of the latter remains unchanged.  Nevertheless, will the spatial hedonic, the entire market model outperforms the OLS model of structural sub-markets.  <i>Flat</i>-based OLS sub-market models benefit substantially from the spatial hedonic.  The results also suggest that an individual accessibility measure is more significant than the zonal measure because it is able to capture the micro effect of location on price.  Further, spatial statistics produce more accurate, robust and reliable estimates of implicit prices.

The Swedish housing market has experienced an almost constant increase of housing prices since the economic crisis in the early 90‟s. Many studies have been conducted on the field which have tried to find an explanation to the constant trend and if there is an end in sight. However, this study aims at focusing on the supply/demand relationship in determining the housing prices in the County of Stockholm. The method that was used was both a time series regression and a cross sectional regression, by applying data on the amount of housing that has been constructed per thousand inhabitants in each municipality, the development of housing prices in each municipality and the average annual development of wages. Since there are 26 municipalities in Stockholm County, it would be too time consuming to go through each and every single one of the municipalities, instead the focus was on the 5 municipalities with the highest and lowest construction rate per thousand inhabitants. Thus, we can observe if there is any general difference depending on the construction rate in determining the house price development. The results on the time series regression implies that most of the municipalities housing prices are primarily dependent on the housing construction rate, when construction goes down the prices goes up and vice versa. However, the municipality of Vallentuna had suspicious signs which imply that other factors (then the variables used) are driving the prices up. In the cross sectional regression where both the 5 highest and lowest municipalities with construction rate were regressed together, we can see similar signs as in Vallentuna. It would therefore be interesting to find out what the underlying factors that are driving the prices up in the case of Vallentuna and in the cross sectional analysis.