Spatial Variation in Job Accessibility and Gender: An Intraregional Analysis using Hedonic House-Price Estimation

PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

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Household demand for improved water supply services in Mekelle City, Northern Ethiopia

Water Policy ◽

10.2166/wp.2010.095 ◽

2011 ◽

Vol 13 (1) ◽

pp. 125-142 ◽

Cited By ~ 3

Author(s):

Kinfe Gebreegziabher ◽

Tewodros Tadesse

Keyword(s):

Formal Education ◽

House Price ◽

Household Survey ◽

Northern Ethiopia ◽

Water Services ◽

Cross Sectional ◽

Housing Status ◽

Water Service ◽

Mekelle City ◽

And Gender

With population growth and urbanization, demand for improved water services has been growing. It is imperative therefore to examine different factors that influence demand for improved water services and the resultant welfare changes. Using cross-sectional household survey data collected through structured questionnaire from ten administrative units in Mekelle City, we estimate household willingness to pay models and identify major determinant factors of demand for improved water service. In order to help us do this, we considered selection issues and estimated models using the Heckman Two-Step Estimator. Our results show that the amount of bid (amount of money households would be willing to pay) that households (already connected to private taps) would be willing to pay is positively associated with household income, ownership of the house, price of vended water and the practice of water purification. For households who are not connected to private taps, the amount they would be willing to pay for (improved) private tap connection is positively associated with formal education, housing status and gender. We also investigate the welfare gains and losses as a result of improved water service. Analytical results show that, as the number of households who subscribe to improved water service increases, there is a gain in surplus for households and revenue (producer surplus) for the municipality.

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Accounting for Spatial Variation of Land Prices in Hedonic Imputation House Price Indices: a Semi-Parametric Approach

Journal of Official Statistics ◽

10.2478/jos-2018-0033 ◽

2018 ◽

Vol 34 (3) ◽

pp. 695-720 ◽

Cited By ~ 1

Author(s):

Yunlong Gong ◽

Jan de Haan

Keyword(s):

Spatial Variation ◽

Price Change ◽

Parametric Method ◽

House Price ◽

Model Performance ◽

Small City ◽

Price Indices ◽

Land Prices ◽

House Price Indices ◽

The Impact

Abstract Location is capitalized into the price of the land the structure of a property is built on, and land prices can be expected to vary significantly across space. We account for spatial variation of land prices in hedonic house price models using geospatial data and a semi-parametric method known as mixed geographically weighted regression. To measure the impact on aggregate price change, quality-adjusted (hedonic imputation) house price indices are constructed for a small city in the Netherlands and compared to price indices based on more restrictive models, using postcode dummy variables, or no location information at all. We find that, while taking spatial variation of land prices into account improves the model performance, the Fisher house price indices based on the different hedonic models are almost identical. The land and structures price indices, on the other hand, are sensitive to the treatment of location.

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House Price Estimation from Visual and Textual Features

Proceedings of the 8th International Joint Conference on Computational Intelligence ◽

10.5220/0006040700620068 ◽

2016 ◽

Cited By ~ 2

Author(s):

Eman H. Ahmed ◽

Mohamed Moustafa

Keyword(s):

House Price ◽

Price Estimation ◽

Textual Features

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House price estimation using an eigenvector spatial filtering approach

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-09-2019-0097 ◽

2019 ◽

Vol 13 (5) ◽

pp. 845-867 ◽

Cited By ~ 2

Author(s):

Michael James McCord ◽

John McCord ◽

Peadar Thomas Davis ◽

Martin Haran ◽

Paul Bidanset

Keyword(s):

Spatial Autocorrelation ◽

House Prices ◽

Spatial Scales ◽

House Price ◽

Spatial Filtering ◽

Spatial Dependency ◽

Content Type ◽

Price Estimation ◽

Eigenvector Spatial Filtering ◽

Filtering Approach

Purpose Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity. Design/methodology/approach Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria. Findings The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error. Originality/value Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

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