Acceso abierto

Mining Real Estate Data: A Systematic Review of Text-Based Approaches

  
09 jul 2025

Cite
Descargar portada

Aggarwal, C. C. ., & Zhai, ChengXiang. (2012). Mining text data. Springer. Search in Google Scholar

Allen, M. (2017). Textual analysis. The SAGE Encyclopedia of Communication Research Methods, 1754–1756. https://doi.org/10.4135/9781483381411 Search in Google Scholar

Antons, D., Grünwald, E., Cichy, P., & Salge, T. O. (2020). The application of text mining methods in innovation research: Current state, evolution patterns, and development priorities. R & D Management, 50(3), 329–351. https://doi.org/10.1111/radm.12408 Search in Google Scholar

Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259–1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x Search in Google Scholar

Arku, R. N., Buttazzoni, A., Agyapon-Ntra, K., & Bandauko, E. (2022). Highlighting smart city mirages in public perceptions: A Twitter sentiment analysis of four African smart city projects. Cities (London, England), 130, 103857. Advance online publication. Search in Google Scholar

Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance-A textual analysis examination. Journal of Real Estate Research, 41(4), 605–638. https://www.jstor.org/stable/10.2307/26857143 https://doi.org/10.22300/0896-5803.41.4.605 Search in Google Scholar

Blei, D. M., Ng, A. Y., & Edu, J. B. (2003). Latent Dirichlet Allocation Michael I. Jordan. Journal of Machine Learning Research, (3), 993–1022. Search in Google Scholar

Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic Approaches to a Successful Literature Review (Second Editon). Sage (Atlanta, Ga.). Search in Google Scholar

Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. In the atlas of AI. Yale University Press., https://doi.org/10.2307/j.ctv1ghv45t https://doi.org/10.12987/9780300252392. Search in Google Scholar

Cui, N., Malleson, N., Houlden, V., Yan, Y., & Comber, A. (2025). Using Twitter to understand spatial-temporal changes in urban green space topics based on structural topic modelling. Cities (London, England), 157, 105601. Advance online publication. https://doi.org/10.1016/j.cities.2024.105601 Search in Google Scholar

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(March), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070 Search in Google Scholar

Elder, J., Miner, G., & Nisbet, B. (2012). Practical text mining and statistical analysis for non-structured text data aplications. Academic Press. Search in Google Scholar

Frederiksen, T., & Banks, G. (2023). Can mining help deliver the SDGs: Discourses, risks and prospects. Journal of Environment & Development, 32(1), 83–106. https://doi.org/10.1177/10704965221139759 Search in Google Scholar

Fu, X., Li, C., & Zhai, W. (2023). Using natural language processing to read plans: A study of 78 resilience plans from the 100 resilient cities network. Journal of the American Planning Association, 89(1), 107–119. https://doi.org/10.1080/01944363.2022.2038659 Search in Google Scholar

Goodwin, K. R. (2021). Thirty years of housing research. Journal of Housing Research, 30(2), 107–110. https://doi.org/10.1080/10527001.2021.1985907 Search in Google Scholar

Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028 Search in Google Scholar

Hearst, M. A. (1999). Untangling text data mining. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, 3–10. www.aaai.org/ https://doi.org/10.3115/1034678.1034679 Search in Google Scholar

Hebdzyński, M. (2023). Quality information gaps in housing listings: Do words mean the same as pictures? Journal of Housing and the Built Environment, 38(4), 2399–2425. https://doi.org/10.1007/s10901-023-10043-z Search in Google Scholar

Heldens, S., Sclocco, A., Dreuning, H., van Werkhoven, B., Hijma, P., Maassen, J., van Nieuwpoort, R. V. (2022). litstudy: A Python package for literature reviews. SoftwareX, 20, 101207. https://doi.org/10.1016/j.softx.2022.101207 Search in Google Scholar

Hohenstatt, R., & Kaesbauer, M. (2014). GECO’s weather forecast for the UK housing market: To what extent can we rely on Google econometrics? Journal of Real Estate Research, 36(2), 253–282. https://doi.org/10.1080/10835547.2014.12091387 Search in Google Scholar

Hotho, A., Nürnberger, A., & Paaß, G. (2005). A brief survey of text mining. Journal for Language Technology and Computational Linguistics, 20(1), 19–62. https://doi.org/10.21248/jlcl.20.2005.68 Search in Google Scholar

Jin, S., Stokes, G., & Hamilton, C. (2023). Empirical evidence of urban climate adaptation alignment with sustainable development: Application of LDA. Cities (London, England), 136, 104254. Advance online publication. https://doi.org/10.1016/j.cities.2023.104254 Search in Google Scholar

Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851. Advance online publication. https://doi.org/10.1016/j.eswa.2020.113851 Search in Google Scholar

Kim, B., Yoo, M., Park, K. C., Lee, K. R., & Kim, J. H. (2021). A value of civic voices for smart city: A big data analysis of civic queries posed by Seoul citizens. Cities (London, England), 108, 102941. Advance online publication. https://doi.org/10.1016/j.cities.2020.102941 Search in Google Scholar

Lawani, A., Reed, M. R., Mark, T., & Zheng, Y. (2019). Reviews and price on online platforms: Evidence from sentiment analysis of Airbnb reviews in Boston. Regional Science and Urban Economics, 75, 22–34. https://doi.org/10.1016/j.regsciurbeco.2018.11.003 Search in Google Scholar

Lin, W., & Yang, F. (2023). The price of short-term housing: A study of Airbnb on 26 regions in the United States. Available at SSRN: https://ssrn.com/abstract=4583093 or http://dx.doi.org/10.2139/ssrn.4583093 Search in Google Scholar

Lula, P. (2005). Text mining jako narzędzie pozyskiwania informacji z dokumentów tekstowych. [Text mining as a tool for obtaining information from text documents]. www.statsoft.pl/czytelnia.html67 Search in Google Scholar

Lula, P. (2018). Statystyczne modelowanie zawartości dokumentów tekstowych [Statistical modeling of the content of text documents]. Wydawnictwo Uniwersytetu Ekonomicznego. Search in Google Scholar

Marchi, V., Marasco, A., & Apicerni, V. (2023). Sustainability communication of tourism cities: A text mining approach. Cities (London, England), 143, 104590. Advance online publication. https://doi.org/10.1016/j.cities.2023.104590 Search in Google Scholar

Mesa-Pedrazas, A., Nogueras-Zondag, R., & Duque-Calvache, R. (2023). The new town square: Twitter discourses about balconies during the 2020 lockdown in Spain. Cities (London, England), 143, 104595. Advance online publication. https://doi.org/10.1016/j.cities.2023.104595 Search in Google Scholar

Mleczko, M., & Desmond, M. (2023). Using natural language processing to construct a national zoning and land use database. Urban Studies (Edinburgh, Scotland), 60(13), 2564–2584. https://doi.org/10.1177/00420980231156352 PMID:39822385 Search in Google Scholar

Ploessl, F., & Just, T. (2024). News coverage vs sentiment: Evaluating German residential real estate markets. International Journal of Housing Markets and Analysis, 17(2), 395–417. https://doi.org/10.1108/IJHMA-07-2022-0102 Search in Google Scholar

Pryce, G., & Oates, S. (2008). Rhetoric in the language of real estate marketing. Housing Studies, 23(2), 319–348. https://doi.org/10.1080/02673030701875105 Search in Google Scholar

Renigier -Biłozor, M., & Janowski, A. (2024). Human-machine synergy in real estate similarity concept. Real Estate Management and Valuation, 32(2), 13-30. DOI: https://doi.org/10.2478/remav-2024-0010 Search in Google Scholar

Renigier-Biłozor, M., Janowski, A., Walacik, M., & Chmielewska, A. (2022). Human emotion recognition in the significance assessment of property attributes. Journal of Housing and the Built Environment, 37, 23–56. https://doi.org/10.1007/s10901-021-09833-0 Search in Google Scholar

Ritchie, S. (2020). Science fictions: Exposing fraud, bias, negligence and hype in science. Random House. Search in Google Scholar

Roberts, H., Sadler, J., & Chapman, L. (2019). The value of Twitter data for determining the emotional responses of people to urban green spaces: A case study and critical evaluation. Urban Studies (Edinburgh, Scotland), 56(4), 818–835. https://doi.org/10.1177/0042098017748544 Search in Google Scholar

Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082. https://doi.org/10.1111/ajps.12103 Search in Google Scholar

Solorzano, G., & Plevris, V. (2022). Computational intelligence methods in simulation and modeling of structures: A state-ofthe-art review using bibliometric maps. Frontiers in Built Environment, 8(8), 1049616. Advance online publication. https://doi.org/10.3389/fbuil.2022.1049616 Search in Google Scholar

Stephens-Davidowitz, S. (2017). Everybody lies: What the internet can tell us about who we really are. Bloomsbury Publishing. Search in Google Scholar

Tang, X., Wang, W. J., & Liu, W. X. (2024). Land revenue and government myopia: Evidence from Chinese cities. Cities (London, England), 154, 105393. Advance online publication. https://doi.org/10.1016/j.cities.2024.105393 Search in Google Scholar

Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x Search in Google Scholar

Tomczyk, P., Brüggemann, P., & Doligalski, T. (2024). The automation of science? Possibilities and boundaries of ai applications for conducting systematic literature reviews. https://Doi.Org/10.1142/S0218213024500234 Search in Google Scholar

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14, 207–222. https://doi.org/10.1111/1467-8551.00375 Search in Google Scholar

van Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651. https://doi.org/10.1002/asi.21075 Search in Google Scholar

van Eck, N. J., & Waltman, L. (2014a). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8(4), 802–823. https://doi.org/10.1016/j.joi.2014.07.006 Search in Google Scholar

van Eck, N. J., & Waltman, L. (2014b). Systematic retrieval of scientific literature based on citation relations: Introducing the CitNetExplorer tool (pp. 13–20). BIR@ ECIR. www.citnetexplorer.nl Search in Google Scholar

Winson-Geideman, K. (2018). Sentiments and Semantics. Source: Journal of Real Estate Literature, 26(1), 3–12. https://doi.org/10.1080/10835547.2018.12090471 Search in Google Scholar

Yang, L., Marmolejo Duarte, C., & Marti Ciriquian, P. (2022). Quantifying the relationship between public sentiment and urban environment in Barcelona. Cities (London, England), 130, 103977. Advance online publication. https://doi.org/10.1016/j.cities.2022.103977 Search in Google Scholar

Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Negocios y Economía, Economía política, Economía política, otros