Modeling Submarket Effect for Real Estate Hedonic Valuation: A Probabilistic Approach

Liu, Zhicheng; Jun, Cao; Xie, Renjie; Yang, Junyan; Wang, Qiao

doi:10.1109/tkde.2020.3010548

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…It is argued that to obtain unbiased estimates of the implicit prices identifying segmentation of the property market is essential. Some studies, namely, Adair et al (1996), Watkins (2001), Berry et al (2003), Lipscomb and Farmer (2005), Tu et al (2007), Liu et al (2020), andNishi et al (2021) utilized hedonic price techniques to test for market segmentation. They recognised the importance of both spatial and structural characteristics when defining submarkets and observed greater levels of homogeneity in the sample at a submarket level.…”

Section: Related Literaturementioning

confidence: 99%

House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

Yazdani

2021

Preprint

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In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.

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Section: Related Literaturementioning

confidence: 99%

House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

Yazdani

2021

Preprint

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“…This study adopts a k-means clustering algorithm to group similar observations (sales records of land in this study) into several distinct clusters, which serve as real estate submarkets for the purpose of price estimation. Several studies utilized price estimation results to test market segmentation, and they proved that market delineations improved the accuracy of price estimation [35][36][37].…”

Section: Application To Real Estate Market Delineationmentioning

confidence: 99%

Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation

Lee

2021

Adv. technol. innov.

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Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. To apply clustering analysis to real estate datasets, this study proposes an entity embedding approach that transforms categorical variables into vector representations. Three variants of a clustering algorithm, i.e., the clustering based on the traditional Euclidean distance, the Gower distance, and the embedding vectors, are applied to the land sales records to delineate the real estate market in Gwacheon-si, Gyeonggi province, South Korea. Then, the relevance of the resultant submarkets is evaluated using the root mean squared errors (RMSE) obtained from a hedonic pricing model. The results show that the RMSE in the embedding vector-based algorithm decreases substantially from 0.076-0.077 to 0.069. This study shows that the clustering algorithm empowered by embedding vectors outperforms the conventional algorithms, thereby enhancing the relevance of the delineated submarkets.

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“…K-means is one example of a clustering algorithms used for housing market segmentation [5,14,25]. Other algorithms applied more recently to submarket delineation include fuzzy c-means [26], density based spatial clustering [27], and probabilistic hierarchical clustering approach using a Bayesian network [28]. Clustering, however, requires vast amounts of individual-level housing data which is costly to acquire and often not available over time.…”

Section: Introductionmentioning

confidence: 99%

Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid

Kenyon,

Arribas-Bel,

Robinson

2024

Land

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The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales.

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Modeling Submarket Effect for Real Estate Hedonic Valuation: A Probabilistic Approach

Cited by 8 publications

References 35 publications

House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation

Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid

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