{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:08:10Z","timestamp":1777705690924,"version":"3.51.4"},"reference-count":44,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,3,4]]},"abstract":"<jats:p>How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03\u20130.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.<\/jats:p>","DOI":"10.3233\/jifs-210917","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T16:50:53Z","timestamp":1634143853000},"page":"4221-4240","source":"Crossref","is-referenced-by-count":5,"title":["Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model"],"prefix":"10.1177","volume":"42","author":[{"given":"Chaojie","family":"Liu","sequence":"first","affiliation":[{"name":"School of Resources and Environment, Henan Agricultural University, Zhengzhou, China"},{"name":"Technical Research Center of Land Regulation and Ecological Reconstruction Engineering of Henan Province, Zhengzhou, China"}]},{"given":"Jie","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Henan Agricultural University, Zhengzhou, China"},{"name":"Technical Research Center of Land Regulation and Ecological Reconstruction Engineering of Henan Province, Zhengzhou, China"}]},{"given":"Wenjing","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Henan Agricultural University, Zhengzhou, China"},{"name":"Technical Research Center of Land Regulation and Ecological Reconstruction Engineering of Henan Province, Zhengzhou, China"}]},{"given":"Zhuoyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Henan Agricultural University, Zhengzhou, China"},{"name":"Technical Research Center of Land Regulation and Ecological Reconstruction Engineering of Henan Province, Zhengzhou, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210917_ref1","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.econmod.2014.07.018","article-title":"The impact of the financial crisis on housing prices in China and Taiwan: A quantile regression analysis","volume":"42","author":"Kang","year":"2014","journal-title":"Econ Model"},{"key":"10.3233\/JIFS-210917_ref2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jhe.2016.07.002","article-title":"Housing prices raise wages: Estimating the unexpected effects of land supply regulation in China","volume":"33","author":"Liang","year":"2016","journal-title":"J Hous Econ"},{"key":"10.3233\/JIFS-210917_ref3","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.apgeog.2016.12.003","article-title":"Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique","volume":"79","author":"Wang","year":"2017","journal-title":"Appl Geogr"},{"key":"10.3233\/JIFS-210917_ref4","first-page":"0264","article-title":"How do population inflow and social infrastructure affect urban vitality? 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