Spatial Modeling and Geovisualization of Rental Prices for Real Estate Portals

Schernthanner, Harald; Asche, Hartmut; Gonschorek, Julia; Scheele, Lasse

doi:10.1007/978-3-319-42111-7_11

Cited by 15 publications

(14 citation statements)

References 21 publications

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“…These shapefiles contain neighborhood boundaries for major cities in 41 states plus DC. This fairly new data set comprises nearly 7,000 neighborhoods, but as Schernthanner et al (2016) point out, Zillow does not publish the methodology used to construct these boundaries. However, despite its newness it already has a track record in the academic literature: Besbris et al (2015) use Zillow boundaries to examine neighborhood stigma and Albrecht and Abramovitz (2014) use them to study neighborhood-level poverty in New York.…”

Section: Study Sites and Data Acquisitionmentioning

confidence: 99%

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

Boeing¹

2018

Environment and Planning B: Urban Analytics and City Science

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OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales-namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

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Section: Study Sites and Data Acquisitionmentioning

confidence: 99%

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

Boeing¹

2018

Environment and Planning B: Urban Analytics and City Science

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“…The use of GIS tools has been particularly significant for the evaluation of the impact of environmental/spatial attributes in property values [1][2][3]. This has resulted in the introduction of advanced geostatistical methods and Geographically Weighted Regression (GWR) as efficient methodologies for capturing spatial heterogeneity and spatial autocorrelation in housing markets versus multiple regression as a global model [4,5]. Further developments in hedonic models of spatial housing economics that can be potentially facilitated with the GIS data environment include elements with extended spatial econometrics, neighbourhood and segregation models, housing market areas, models of segregation, migration, agent-based models and utilization of recently developed machine learning and data mining techniques [6].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

Čeh

Kilibarda

Lisec

et al. 2018

IJGI

157

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Abstract:The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008-2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R 2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.

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“…In contrast to the sales market, the valuation of properties in the rental market has received little academic study [19, 20]. This has primarily been due to the lack of available data on such transactions, with sales price data having been the priority for data capture by Government and State authorities.…”

Section: Introductionmentioning

confidence: 99%

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

Clark

Lomax

2018

J Big Data

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Introduction Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commercial organisations and local and national governments in understanding this market. Case description This case study uses data that are supplied from a property listings web site and are unique in their scale, with over 1.2 million rental property listings available over a 2 year period. The data is analysed in a large data institute using generalised linear regression, machine learning and a pseudo practitioner based approach. Discussion and evaluation The study should be seen as a practical guide for property professionals and academics wishing to undertake such appraisals and looking for guidance on the best methods to use. It also provides insight into the property characteristics which most influence rental listing price. Conclusions From the regression analysis, attributes that increase the rental listing price are: the number of rooms in the property, proximity to central London and to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. Of the machine learning algorithms used, the two tree based approaches were seen to outperform the regression based approaches. In terms of a simple measure of the median appraisal error, a practitioner based approach is seen to outperform the modelling approaches. A practical finding is that the application of sophisticated machine learning algorithms to big data is still a challenge for modern desktop PCs.

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Spatial Modeling and Geovisualization of Rental Prices for Real Estate Portals

Cited by 15 publications

References 21 publications

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

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