IDEAS home Printed from https://ideas.repec.org/r/jre/issued/v22n32001p313-336.html
   My bibliography  Save this item

Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
  2. Mehmet Emin Tabar & Aziz Sisman & Yasemin Sisman, 2023. "A Real Estate Appraisal Model with Artificial Neural Networks and Fuzzy Logic: A Local Case Study of Samsun City," International Real Estate Review, Global Social Science Institute, vol. 26(4), pages 565-581.
  3. Alla Koblyakova & Larisa Fleishman & Orly Furman, 2022. "Accuracy of Households’ Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case," The Journal of Real Estate Finance and Economics, Springer, vol. 65(1), pages 48-74, July.
  4. Ansgar Belke & Jonas Keil, 2018. "Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 24(1), pages 25-45, February.
  5. Demetriou, Demetris, 2018. "Automating the land valuation process carried out in land consolidation schemes," Land Use Policy, Elsevier, vol. 75(C), pages 21-32.
  6. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
  7. Maurizio d’Amato, 2007. "Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 42-65.
  8. Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria, 2020. "Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”," MPRA Paper 104234, University Library of Munich, Germany, revised 19 Nov 2020.
  9. Wang, Dan & Tang, Yu-Ting & He, Jun & Yang, Fei & Robinson, Darren, 2021. "Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)," Energy, Elsevier, vol. 216(C).
  10. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
  11. repec:ipg:wpaper:2014-473 is not listed on IDEAS
  12. Gang-Zhi Fan & Seow Eng Ong & Hian Chye Koh, 2006. "Determinants of House Price: A Decision Tree Approach," Urban Studies, Urban Studies Journal Limited, vol. 43(12), pages 2301-2315, November.
  13. Christian Pierdzioch, 2012. "Macroeconomic Factors and the German Real Estate Market: A Stock-Market-Based Forecasting Experiment," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 87-96, May.
  14. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
  15. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
  16. Koen Koning & Tatiana Filatova & Okmyung Bin, 2018. "Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 247-263, February.
  17. Kuan-Lun Pan & Hsiao Jung Teng & Shih-Yuan Lin & Yu En Cheng, 2021. "An Empirical Method for Decomposing the Contributions of Land and Building Values to Housing Value," International Real Estate Review, Global Social Science Institute, vol. 24(3), pages 385-403.
  18. Craig Ellis & Patrick J. Wilson & Ralf Zurbruegg, 2007. "Real Estate ‘Value’ Stocks and International Diversification," Journal of Property Research, Taylor & Francis Journals, vol. 24(3), pages 265-287, September.
  19. Elena B. Pokryshevskaya & Evgeny A. Antipov, 2011. "Applying a CART-based approach for the diagnostics of mass appraisal models," Economics Bulletin, AccessEcon, vol. 31(3), pages 2521-2528.
  20. Bovkir, Rabia & Aydinoglu, Arif Cagdas, 2018. "Providing land value information from geographic data infrastructure by using fuzzy logic analysis approach," Land Use Policy, Elsevier, vol. 78(C), pages 46-60.
  21. Demetris Demetriou, 2017. "A spatially based artificial neural network mass valuation model for land consolidation," Environment and Planning B, , vol. 44(5), pages 864-883, September.
  22. R. Kelley Pace & Darren Hayunga, 2020. "Examining the Information Content of Residuals from Hedonic and Spatial Models Using Trees and Forests," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 170-180, February.
  23. Yan Kestens & Marius Thériault & François Des Rosiers, 2004. "The Impact of Surrounding Land Use and Vegetation on Single-Family House Prices," Environment and Planning B, , vol. 31(4), pages 539-567, August.
  24. Renigier-Biłozor, Małgorzata & Źróbek, Sabina & Walacik, Marek & Borst, Richard & Grover, Richard & d’Amato, Maurizio, 2022. "International acceptance of automated modern tools use must-have for sustainable real estate market development," Land Use Policy, Elsevier, vol. 113(C).
  25. Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
  26. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
  27. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
  28. Shawn L. Robey & Mark A McKnight & Misty R. Price & Rachel N. Coleman, 2019. "Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study," Accounting and Finance Research, Sciedu Press, vol. 8(2), pages 1-99, May.
  29. Jose Torres-Pruñonosa & Pablo García-Estévez & Camilo Prado-Román, 2021. "Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing," Mathematics, MDPI, vol. 9(7), pages 1-16, April.
  30. Wan, Wayne Xinwei & Lindenthal, Thies, 2022. "Towards accountability in machine learning applications: A system-testing approach," ZEW Discussion Papers 22-001, ZEW - Leibniz Centre for European Economic Research.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.