Abstract
The e-commerce boom has shifted the real estate market to an online medium whereby realtors and owners can post their property listings. A recent focus of the research community has been studying various aspects of these online property portals. We perform a textual analytics study for a popular Irish portal with Dublin being our case-study on account of its severe housing crisis and heterogeneous regions. We extract various textual features from within the property advertisements and analyse their correlations with the deprivation index of the location in which the property is located thereby attempting to understand how affluence of a location influences the way in which advertisements are framed, and thereby aiding the user towards the interpretation of property advertisements. The correlation analysis reveals interesting outcomes depicting tendency of realtors/owners to over emphasize location aspects of a property for affluent locations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
This is particularly relevant for location-based features.
- 2.
This will be explained in detail in Sect. 3.
- 3.
MLS is the singularly most important database in United States where real estate agents and brokers list real estate properties for sale.
- 4.
Note that a majority of these used online real estate portals multiple times on account of moving house.
- 5.
- 6.
- 7.
Note that relative index score is included as part of the Pobal index.
- 8.
References
Brown, V.J.: Give me shelter: the global housing crisis. Environ. Health Perspect. 111(2), A92 (2003)
Buonanno, P., Montolio, D., Raya-Vílchez, J.M.: Housing prices and crime perception. Empir. Econ. 45(1), 305–321 (2013)
Byrne, M., et al.: The financialization of housing and the growth of the private rental sector in Ireland, the UK and Spain. Technical report (2019)
Carstairs, V.: Deprivation indices: their interpretation and use in relation to health. J. Epidemiol. Community Health 49(Suppl 2), S3–S8 (1995)
Cortright, J.: Walking the walk: How walkability raises home values in US cities (2009)
De Nadai, M., Lepri, B.: The economic value of neighborhoods: predicting real estate prices from the urban environment. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 323–330. IEEE (2018)
Elliot, P., Cuzick, J.: Geographical and Environmental Epidemiology: Methods for Small Area Studies. World Health Organization, Geneva (1992). (Number 614.42 G3)
Fu, Y., et al.: Sparse real estate ranking with online user reviews and offline moving behaviors. In: 2014 IEEE International Conference on Data Mining, pp. 120–129. IEEE (2014)
Gelman, I.A., Wu, N.: Combining structured and unstructured information sources for a study of data quality: a case study of Zillow.com. In: 2011 44th Hawaii International Conference on System Sciences (HICSS), pp. 1–12. IEEE (2011)
Haase, T., Pratschke, J.: The 2016 Pobal HP Deprivation Index (SA) (2016)
Haase, T., Pratschke, J., Gleeson, J.: The 2011 All-Island HP Deprivation Index. Pobal, Dublin (2014)
Hearne, R.: A home or a wealth generator. Inequality, financialisation and the Irish housing crisis. TASC, Dublin (2017)
Hristova, D., Aiello, L.M., Quercia, D.: The new urban success: how culture pays. Front. Phys. 6, 27 (2018)
Keenan, M.: Revealed: how much living close to Luas and Dart adds to your house price (2018). https://www.independent.ie/irish-news/revealed-how-much-living-close-to-luas-and-dart-adds-to-your-house-price-36933179.html
Kiel, K.A., Zabel, J.E.: Location, location, location: The 3L approach to house price determination. J. Hous. Econ. 17(2), 175–190 (2008)
Kontrimas, V., Verikas, A.: The mass appraisal of the real estate by computational intelligence. Appl. Soft Comput. 11(1), 443–448 (2011)
Krainer, J., Wei, C., et al.: House prices and fundamental value. FRBSF Econ. Lett. (2004)
Lam, E.: Modern regression models and neural networks for residential property valuation. Royal Institute of Chartered Surveyors (1996)
Li, X.-B., Motiwalla, L.: For sale by owner online: who gets the saved commission? Commun. ACM 52(2), 110–114 (2009)
Nagaraja, C.H., Brown, L.D., Zhao, L.H., et al.: An autoregressive approach to house price modeling. Ann. Appl. Stat. 5(1), 124–149 (2011)
Nowak, A., Smith, P.: Textual analysis in real estate. J. Appl. Econ. (2016)
Nowak, A., Smith, P.: Textual analysis in real estate. J. Appl. Econ. 32(4), 896–918 (2017)
O’Brien, C.: Dublin’s north/south divide apparent in property prices (2018). https://www.irishtimes.com/business/construction/dublin-s-north-south-divide-apparent-in-property-prices-1.3660146. (Irish Times)
Orford, S.: Valuing location in an urban housing market. In: The Proceedings of the 3rd International Conference on GeoComputation, United Kingdom, University of Bristol, Bristol, BS8 1SS, UK, pp. 56–58 (1988)
Poursaeed, O., Matera, T., Belongie, S.: Vision-based real estate price estimation. Mach. Vis. Appl. 29(4), 667–676 (2018)
Shahbazi, M., Barr, J.R., Hristidis, V., Srinivasan, N.N.: Estimation of the investability of real estate properties through text analysis. In: 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), pp. 301–306. IEEE (2016)
Taylor, L.O.: The hedonic method. In: Champ, P.A., Boyle, K.J., Brown, T.C. (eds.) A Primer on Nonmarket Valuation, pp. 331–393. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-007-0826-6_10
Wong, C.: Indicators for Urban and Regional Planning: The Interplay of Policy and Methods. Routledge, Abingdon (2006)
You, Q., Pang, R., Cao, L., Luo, J.: Image-based appraisal of real estate properties. IEEE Trans. Multimedia 19(12), 2751–2759 (2017)
Acknowledgments
This work is supported by Science Foundation Ireland through the Insight Centre for Data Analytics under SFI/12/RC/2289_P2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Younus, A., Qureshi, M.A., O’Mahony, M. (2019). Housing in Affluent vs. Deprived Areas: An Analysis of Online Housing Advertisements in Dublin. In: Weber, I., et al. Social Informatics. SocInfo 2019. Lecture Notes in Computer Science(), vol 11864. Springer, Cham. https://doi.org/10.1007/978-3-030-34971-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-34971-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34970-7
Online ISBN: 978-3-030-34971-4
eBook Packages: Computer ScienceComputer Science (R0)