{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:56:10Z","timestamp":1772762170747,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,12]],"date-time":"2016-01-12T00:00:00Z","timestamp":1452556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National HighTechnology Research and Development Program (&quot;863&quot;Program) of China","award":["No.2012AA12A211"],"award-info":[{"award-number":["No.2012AA12A211"]}]},{"name":"National HighTechnology Research and Development Program (&quot;863&quot;Program) of China","award":["2012AA12A402"],"award-info":[{"award-number":["2012AA12A402"]}]},{"name":"National HighTechnology Research and Development Program (&quot;863&quot;Program) of China","award":["2013AA12A403"],"award-info":[{"award-number":["2013AA12A403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. Housing prices in Beijing are assessed to validate the feasibility of the proposed model. The COGWR model demonstrated a better goodness-of-fit than the GWR when housing price data were limited because a COGWR is able to effectively absorb no-price data with explanatory variables into its learning by considering spatial variations and nonstationarity that may introduce significant biases into housing prices. This result demonstrates that a semisupervised geographic weighted regression may be effectively used to predict housing prices.<\/jats:p>","DOI":"10.3390\/ijgi5010004","type":"journal-article","created":{"date-parts":[[2016,1,12]],"date-time":"2016-01-12T10:19:25Z","timestamp":1452593965000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing"],"prefix":"10.3390","volume":"5","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghua","family":"Xu","sequence":"additional","affiliation":[{"name":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/00049180500150019","article-title":"Segmentation of the housing market and its determinants: Seoul and its neighbouring new towns in Korea","volume":"36","author":"Kim","year":"2005","journal-title":"J. Aust. Geogr."},{"key":"ref_2","first-page":"65","article-title":"Determinants of house prices in Turkey: A hedonic regression model","volume":"9","author":"Selim","year":"2011","journal-title":"J. Do\u011fu\u015f \u00dcniv. Dergisi."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1111\/tgis.12020","article-title":"Using contextualized geographically weighted regression to model the spatial heterogeneity of land prices in Beijing, China","volume":"17","author":"Harris","year":"2013","journal-title":"Trans. GIS"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1080\/13658816.2013.865739","article-title":"Geographically weighted regression with a non-euclidean distance metric: A case study using hedonic house price data","volume":"28","author":"Lu","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1080\/13658816.2013.878463","article-title":"A geographically and temporally weighted autoregressive model with application to housing prices","volume":"28","author":"Wu","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1111\/0022-4146.00146","article-title":"Some notes on parametric significance tests for geographically weighted regression","volume":"39","author":"Brunsdon","year":"1999","journal-title":"J. Reg. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically weighted regression: A method for exploring spatial nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s101090200081","article-title":"Analysing regional industrialisation in Jiangsu province using geographically weighted regression","volume":"4","author":"Huang","year":"2002","journal-title":"J. Geogr. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/10835547.2010.12091276","article-title":"Predicting house prices with spatial dependence: A comparison of alternative methods","volume":"32","author":"Bourassa","year":"2010","journal-title":"J. Real. Estate Ers"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1006\/jhec.1998.0236","article-title":"Spatial autocorrelation: A primer","volume":"7","author":"Dubin","year":"1998","journal-title":"J. Hous. Econ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.regsciurbeco.2008.11.001","article-title":"How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing markets","volume":"39","author":"Redfearn","year":"2009","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1177\/0042098013492234","article-title":"Spatial heterogeneity in hedonic house price models\u2014The case of Austria","volume":"51","author":"Helbich","year":"2014","journal-title":"Urban Stud."},{"key":"ref_13","first-page":"19","article-title":"An introduction to spatial econometrics","volume":"123","author":"LeSage","year":"2008","journal-title":"Rev. Econ. Ind."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1111\/1540-6229.00748","article-title":"Generalizing the OLS and grid estimators","volume":"26","author":"Pace","year":"1998","journal-title":"R. Estate Econ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S1051-1377(03)00031-7","article-title":"Housing market segmentation and hedonic prediction accuracy","volume":"12","author":"Goodman","year":"2003","journal-title":"J. Hous. Econ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Blum, A., and Mitchell, T. (1998, January 24\u201326). Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computational Learning Theory (ACM), Madison, WI, USA.","DOI":"10.1145\/279943.279962"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Brefeld, U., G\u00e4rtner, T., Scheffer, T., and Wrobel, S. (2006, January 25\u201329). Efficient co-regularised least squares regression. Proceedings of the 23rd International Conference on Machine Learning (ACM), Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143862"},{"key":"ref_18","unstructured":"Zhou, Z.H., and Li, M. (August, January 30). Semi-supervised regression with co-training. Proceedings of 2005 International Joint Conferences on Artificial Intelligence, Edinburgh, Scotland."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/TKDE.2007.190644","article-title":"Semisupervised regression with cotraining-style algorithms","volume":"19","author":"Zhou","year":"2007","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.08.003","article-title":"An efficient semi-supervised classification approach for hyperspectral imagery","volume":"97","author":"Tan","year":"2014","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices","volume":"24","author":"Huang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1006\/juec.1997.2071","article-title":"Andrew Court and the invention of hedonic price analysis","volume":"44","author":"Goodman","year":"1998","journal-title":"J. Urban Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1068\/b32119","article-title":"Modeling spatial dimensions of housing prices in Milwaukee, WI","volume":"34","author":"Yu","year":"2007","journal-title":"Environ. Plan. B"},{"key":"ref_24","unstructured":"Ustao\u011flu, E. (2003). Hedonic Price Analysis of Office Rents: A Case Study of the Office Market in Ankara. [Master Thesis, Middle East Technical University]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1086\/260169","article-title":"Hedonic prices and implicit markets: Product differentiation in pure competition","volume":"82","author":"Rosen","year":"1974","journal-title":"J. Polit. Econ."},{"key":"ref_26","first-page":"405","article-title":"A comparison of localized regression models in a hedonic house price context","volume":"29","author":"Farber","year":"2006","journal-title":"Can. J. Reg. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1111\/j.1467-9787.2010.00664.x","article-title":"Estimation and hypothesis testing for nonparametric hedonic house price functions","volume":"50","author":"McMillen","year":"2010","journal-title":"J. Reg. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1080\/00045608.2012.707587","article-title":"Data-driven regionalization of housing markets","volume":"103","author":"Helbich","year":"2013","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1080\/10835547.2008.12091225","article-title":"An adaptive neuro-fuzzy inference system based approach to real estate property assessment","volume":"30","author":"Guan","year":"2008","journal-title":"J. Real Estate Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1080\/10835547.2009.12091245","article-title":"Neural network hedonic pricing models in mass real estate appraisal","volume":"31","author":"Peterson","year":"2009","journal-title":"J. Real Estate Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1016\/j.eswa.2009.07.031","article-title":"The use of fuzzy logic in predicting house selling price","volume":"37","author":"Aytekin","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10109-005-0011-8","article-title":"Heterogeneity in hedonic modelling of house prices: Looking at buyers\u2019 household profiles","volume":"8","author":"Kestens","year":"2006","journal-title":"J. Geogr. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.jue.2007.04.010","article-title":"Land and residential property markets in a booming economy: New evidence from Beijing","volume":"63","author":"Zheng","year":"2008","journal-title":"J. Urban Econ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10115-009-0209-z","article-title":"Semi-supervised learning by disagreement","volume":"24","author":"Zhou","year":"2010","journal-title":"Knowl. Inf. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1177\/0042098008091491","article-title":"Moving window approaches for hedonic price estimation: An empirical comparison of modelling techniques","volume":"45","author":"Long","year":"2008","journal-title":"Urban Stud."},{"key":"ref_36","unstructured":"Goldman, S., and Zhou, Y. (1990, January 21\u201323). Enhancing supervised learning with unlabeled data. Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","article-title":"Tri-training: Exploiting unlabeled data using three classifiers","volume":"17","author":"Zhou","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TSMCA.2007.904745","article-title":"Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples","volume":"37","author":"Li","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. A"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1068\/a38325","article-title":"Diagnostic tools and a remedial method for collinearity in geographically weighted regression","volume":"39","author":"Wheeler","year":"2007","journal-title":"Environ. Plan. A"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10109-005-0155-6","article-title":"Multicollinearity and correlation among local regression coefficients in geographically weighted regression","volume":"7","author":"Wheeler","year":"2005","journal-title":"J. Geogr. Syst."},{"key":"ref_41","unstructured":"David, B. (1991). Conditioning Diagnostics, Collinearity and Weak Data in Regression, John Wiley & Sons."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1068\/a3162","article-title":"Statistical tests for spatial nonstationarity based on the geographically weighted regression model","volume":"32","author":"Leung","year":"2000","journal-title":"Environ. Plan. A"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s11258-010-9769-y","article-title":"Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe catchment, China","volume":"209","author":"Zhao","year":"2010","journal-title":"Plant Ecol."},{"key":"ref_44","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationship, Wiley."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:17:35Z","timestamp":1760210255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,12]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1]]}},"alternative-id":["ijgi5010004"],"URL":"https:\/\/doi.org\/10.3390\/ijgi5010004","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,12]]}}}