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Author: 羅奕宏
Publisher:
ISBN:
Category : Housing
Languages : en
Pages : 0
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Author: 羅奕宏
Publisher:
ISBN:
Category : Housing
Languages : en
Pages : 0
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Author: Yet-Fhang Daniel Lo
Publisher: Open Dissertation Press
ISBN: 9781361326947
Category :
Languages : en
Pages :
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This dissertation, "Factors Affecting Spatial Autocorrelation in Housing Prices: an Empirical Study of Hong Kong" by Yet-fhang, Daniel, Lo, 羅奕宏, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Real estate economists and practitioners have been cognizant of spatial autocorrelation in housing prices for more than three decades. In the early days, they relied to a huge extent on techniques developed in statistical science and focused exclusively on its identification and quantitative assessment in housing studies. It has been well-acknowledged that the presence of spatial autocorrelation in housing prices will compromise the applicability of conventional hedonic statistics, which may lead to biased and inconsistent estimates. In light of this, recent studies in the area have spawned an immense literature aimed at devising sophisticated econometric models such as hedonic spatial lag model and hedonic error model as correction methods. Interestingly, the underpinning factors attributing to its existence remain relatively theoretically unexplored due perhaps to a paucity of quality goereferenced housing data as well as the indifferent attitude espoused by the researchers. Although some general propositions regarding its causes have been proposed, they are accused of lacking inferential basis. Acknowledging the above research gap, this thesis attempts to investigate the underlying factors affecting the formation of, and change in, spatial autocorrelation in housing prices. Specifically, we conjecture that spatial autocorrelation is crucially determined by one of the economic workings of the housing market-price determination process. It is posited that the occurrence of the spatial phenomenon is a direct consequence of how market participants in search of past information in the market ascertain current housing prices. Specifically, spatial autocorrelation is deemed to be established, or increase, when property traders infer current housing prices from past sales of properties (i.e. comparables) located in the same neighborhood as the subject houses. Based on the above information search framework, we put forward three hypotheses to facilitate our examination. First, it is hypothesized that market volatility depresses spatial autocorrelation. As in any other commodity market, past sales transactions in real estate are important sources of price information, which become more obsolescent, and hence, fail to be a good price signal when the market is more volatile. Traders are compelled to rely less on past sales in establishing the current prices of the housing units. Accordingly, spatial autocorrelation will be diminished; following broadly the same line of logic, the second hypothesis is constructed, which states that market liquidity (defined as total market transaction volume) dampens spatial autocorrelation. Given that market liquidity reflects the amount of price information being circulated in the market, traders in accessing property values in a "thick" market do not have to necessarily infer from comparables that are located further away from the subject properties. Hence, a weaker spatial autocorrelation relationship between prices is resulted; third, building age is a critical factor in assessing a house's redevelopment potential, whose value is largely independent of the transaction prices of the surrounding housing units. Given that a house's total value can be perceived as an addition of its use value and redevelopment value (i.e. real option value of redevelopment), and that the former's role decreases whereas the latter's increases
Author: Wenzheng Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 194
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Book Description
Using 16126 second-hand commercial housing samples collecting by web-crawl program in Beijing, China, this study develops traditional hedonic price model and spatial econometric models to quantitatively evaluate influential factors of housing prices in terms of housing structure, environmental and locational factors. Results show that locational factors, such as accessibility to subway, CBDs and schools, outperform other non-spatial factors as 6 locational variables explain 64% of variance. Most of housing structure variables have expected sign except floor position which manifests Beijing customers prefer middle floor. This finding does not agree with the evidence from southern Chinese cities where high floor charges higher price. We convince that this idiosyncrasy of Beijing reflects customers’ concern to safety during disasters such as earthquake and fire. Results also demonstrate customers’ desire to higher green ratio within community and closer distance to parks. We develop a feasible methodology for acquiring accurate NDVI in urban environment and test its validity in Beijing for the first time. We also demonstrate the existence of spatial error and spatial lag dependence in Beijing housing price using Lagrange Multiplier and successfully eliminate spatial dependence using spatial error (SEM) and spatial lag Model (SLM). The comparison of OLS and SEM suggests that OLS has overestimated the capitalization of housing structure and educational factors, whereas underestimated the effects of environmental, transportation and commute factors. The significant Rho (74.88%) in SLM model demonstrates the spatial spill-over effects caused by the interaction of nearby housing price and this effect is greater than other Chinese cities, such as Chengdu (Rho=19.2%) and Hangzhou (Rho=45.7%). This phenomenon can be partially explained by the potential speculation and booming of Beijing housing market. Key Words: web-crawl program; hedonic price model; spatial regression; spatial autocorrelation; spatial spill-over effect.
Author: Fei Long
Publisher:
ISBN:
Category :
Languages : en
Pages : 274
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Book Description
Author: Daniel A. Griffith
Publisher: Springer Science & Business Media
ISBN: 9780792349150
Category : Business & Economics
Languages : en
Pages : 222
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Book Description
The purpose of models is not to fit the data but to sharpen the questions. S. Karlin, 11th R. A. Fisher Memorial Lecture, Royal Society, 20 April 1983 We are proud to offer this volume in honour of the remarkable career of the Father of Spatial Econometrics, Professor Jean Paelinck, presently of the Tinbergen Institute, Rotterdam. Not one to model solely for the sake of modelling, the above quotation nicely captures Professor Paelinck's unceasing quest for the best question for which an answer is needed. His FLEUR model has sharpened many spatial economics and spatial econometrics questions! Jean Paelinck, arguably, is the founder of modem spatial econometrics, penning the seminal introductory monograph on this topic, Spatial Econometrics, with Klaassen in 1979. In the General Address to the Dutch Statistical Association, on May 2, 1974, in Tilburg, "he coined the term [spatial econometrics] to designate a growing body of the regional science literature that dealt primarily with estimation and testing problems encountered in the implementation of multiregional econometric models" (Anselin, 1988, p. 7); he already had introduced this idea in his introductory report to the 1966 Annual Meeting of the Association de Science Regionale de Langue Fran~aise.
Author: Geoffrey Meen
Publisher: Springer Science & Business Media
ISBN: 1461516730
Category : Business & Economics
Languages : en
Pages : 279
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Book Description
Spatial fixity is one of the characteristics that distinguishes housing from most other goods and services in the economy. In general, housing cannot be moved from one part of the country to another in response to shortages or excesses in particular areas. The modelling of housing markets and the interlinkages between markets at different spatial levels - international, national, regional and urban - are the main themes of this book. A second major theme is disaggregation, not only in terms of space, but also between households. The book argues that aggregate time-series models of housing markets of the type widely used in Britain and also in other countries in the past have become less relevant in a world of increasing income dispersion. Typically, aggregate relationships will break down, except under special conditions. We can no longer assume that traditional location or tenure patterns, for example, will continue in the future. The book has four main components. First, it discusses trends in housing markets both internationally and within nations. Second, the book develops theoretical housing models at each spatial scale, starting with national models, moving down to the regional level and, then, to urban models. Third, the book provides empirical estimates of the models and, finally, the models are used for policy analysis. Analysis ranges over a wide variety of topics, including explanations for differing international house price trends, the causes of housing cycles, the role of credit markets, regional housing market interactions and the role of housing in urban/suburban population drift.
Author: Prodosh Simlai
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We investigate whether spatial idiosyncratic risk plays an important role in explaining average housing prices in a representative U.S. market. We discuss a parsimonious hedonic model of demand for differentiated products and derive an equilibrium price functions that depends on idiosyncratic risk, among other factors. Empirically, we use a nonlinear spatial regression model and identify a potential measure of idiosyncratic risk from sales data of individual residential properties in Ames, Iowa. The results show that, for our disaggregated housing data, there is a significant volatility interdependence among cross-sectional units because of geographical proximity. In our sample, a 1% increase in idiosyncratic risk, ceteris paribus, is associated with a 0.80% increase in average price of residential properties. We find that accounting for spatial autocorrelation and heteroskedasticity increases the evidence that idiosyncratic risk, which is captured by space-varying volatility, reveals important information about average housing prices. We conclude that using a spatial regression model that allows interaction between property prices and volatility yields strong predictive power.
Author: Maurizio d'Amato
Publisher: Springer
ISBN: 3319497464
Category : Technology & Engineering
Languages : en
Pages : 435
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Book Description
This book addresses several problems related to automated valuation methodologies (AVM). Following the non-agency mortgage crisis, it offers a variety of approaches to improve the efficiency and quality of an automated valuation methodology (AVM) dealing with emerging problems and different contexts. Spatial issue, evolution of AVM standards, multilevel models, fuzzy and rough set applications and quantitative methods to define comparables are just some of the topics discussed.
Author: Yunlong Gong
Publisher:
ISBN: 9789492516510
Category :
Languages : en
Pages :
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Book Description
This research discovers the spatial regularities of house prices across Chinese prefecture cities in an economic common area and investigates the underlying formation process. It reveals an uneven distribution of house prices across cities, with those large and/or higher-tier cities and their neighbours having significantly higher house prices. Such an uneven pa����ern of house prices demonstrates the agglomeration spillovers in the interurban housing market. Two forms of spillovers are empirically examined. The first is the urban hierarchy distance effect, which is related to the position of a city in a hierarchical urban system. In general, the distance penalty of higher-tier urban centres is confirmed, that is, all else being equal, the further away a city is from the higher-tier city, the lower the house price. The second form of spillovers relates to a city's position in a city network system, in which no hierarchical structure is imposed. In such a situation, the spillovers arise from the interaction with neighbouring cities and it is found that a city that has larger neighbours tends to have higher house prices. These two forms of spillovers are somewhat correlated with each other because a higher-tier city is always associated with a larger urban size.
Author: Konstantinos P. Vergos
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
This study investigates the spatial statistics of house prices in Beijing, China. We examine whether the house prices in one region is affected by the house price in neighbouring regions. We also investigate how the house prices in one region is affected by unknown characteristics of the neighbouring regions. Moreover, we analyse whether the explanatory factors of house prices in one region are affected by explanatory factors of house prices in neighbouring regions. Subsequently, we attempt to investigate the spatial spill-over effects of explanatory factors. Initially, we use Lagrange Multiplier (LM) test to examine the significance of spatial autocorrelation. After this we apply the spatial autoregressive model (SAR), spatial Durbin model (SDM), spatial autoregressive model with autoregressive disturbances (SAC) and spatial error model (SEM) into spatial regression methods. The paper overcomes the shortcomings of the previous studies by extending the range of examining spatial models, providing reasonable spatial model selection procedures, and employing improved spatial weights to analysing spillover effects of explanatory factors. On the aspect of analysing direct and indirect (spill-over) effects, this study examines the partitioning of direct and indirect effects and finds out the impacts of the neighbouring factors. Evidence is found for spatial dependence of house prices: house prices in one region are influenced by the house prices in neighbouring regions positively and significantly. Evidence is found for spatial heterogeneity of house prices across the space: house prices in neighbouring regions spill over more in times of increasing neighbouring house prices, then when neighbouring house prices are declining. Evidence is found for spatial spillover effects of explanatory factors: increases of average wage of real estate staff, income , tax, urban population and the house prices of the previous year increases the house prices positively in neighbouring regions; a decrease of unemployment drives down the house prices in neighbouring regions.
