scholarly journals Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

2017 ◽  
Vol 6 (11) ◽  
pp. 358 ◽  
Author(s):  
Yixiong Xiao ◽  
Xiang Chen ◽  
Qiang Li ◽  
Xi Yu ◽  
Jin Chen ◽  
...  
Keyword(s):  
Open Access ◽  
Housing Prices ◽  
Hedonic Regression

Open Access: Housing prices and wealth inequality in Western Europe

2021 ◽  
pp. 23-46
Author(s):  
Gregory W. Fuller ◽  
Alison Johnston ◽  
Aidan Regan
Keyword(s):  
Open Access ◽  
Western Europe ◽  
Housing Prices ◽  
Wealth Inequality

scholarly journals Valuation methods for the housing market: Evidence from Budapest

2016 ◽  
Vol 66 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Dávid Kutasi ◽  
Milán Csaba Badics
Keyword(s):  
Neural Network ◽  
Real Estate ◽  
Large Scale ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Hedonic Regression ◽  
Valuation Methods ◽  
Valuation Method

Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.

scholarly journals Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0217505 ◽  
Author(s):  
Xiao Fu ◽  
Tianxia Jia ◽  
Xueqi Zhang ◽  
Shanlin Li ◽  
Yonglin Zhang
Keyword(s):  
Deep Learning ◽  
Open Access ◽  
Housing Prices ◽  
Street Level

scholarly journals Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data

2018 ◽  
Vol 10 (5) ◽  
pp. 1676 ◽  
Author(s):  
Hao Wu ◽  
Hongzan Jiao ◽  
Yang Yu ◽  
Zhigang Li ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
Open Access ◽  
Regression Model ◽  
Housing Prices ◽  
Influence Factors ◽  
Urban Housing ◽  
Open Access Data ◽  
Access Data

scholarly journals Estimation of Housing Price Variations Using Spatio-Temporal Data

2019 ◽  
Vol 11 (6) ◽  
pp. 1551 ◽  
Author(s):  
Jorge Chica-Olmo ◽  
Rafael Cano-Guervos ◽  
Mario Chica-Rivas
Keyword(s):  
Real Estate ◽  
Spatial Variability ◽  
Urban Studies ◽  
Housing Prices ◽  
Housing Price ◽  
Hedonic Regression ◽  
Temporal Data ◽  
Spatio Temporal ◽  
The City ◽  
External Drift

This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.

Open Access – Sie haben die Wahl mit OpenMind

2009 ◽  
Vol 20 (2) ◽  
pp. 47-48
Author(s):  
G.-Jürgen Hogrefe
Keyword(s):  
Open Access

Strukturierte klinische Interviews zur Erfassung psychischer Störungen über die Lebensspanne

2017 ◽  
Vol 46 (3) ◽  
pp. 176-186 ◽  
Author(s):  
Jürgen Margraf ◽  
Jan Christopher Cwik ◽  
Verena Pflug ◽  
Silvia Schneider
Keyword(s):  
Open Access ◽  
Psychische Störungen ◽  
Dsm 5

Zusammenfassung. Psychische Störungen können über die ganze Lebensspanne auftreten. Strukturierte klinische Interviews sind zentrale Hilfsmittel für ihre rasche, zuverlässige und umfassende Diagnostik. Im deutschsprachigen Raum stehen mit den Verfahren der DIPS-Familie Interviews zur Diagnostik psychischer Störungen über die gesamte Lebensspanne zur Verfügung, die seit den 90er Jahren regelmäßig aktualisiert wurden. Ihre Reliabilität, Validität und Akzeptanz wurde wiederholt in großen Stichproben aus ambulanten, stationären und Forschungssettings überprüft. Die Einführung des DSM-5 erforderte eine umfassende Überarbeitung der DIPS-Interviews, deren wesentliche Merkmale dargestellt werden. Um die breitere Verwendung von strukturierten klinischen Interviews zu fördern, werden die Verfahren der DIPS-Familie neu als „Open Access-Dokumente“ zur Verfügung gestellt. Abschließend werden weitere Entwicklungen zu Training, Dissemination und Computerisierung im Ausblick angesprochen.

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