Skip to main content
SpringerLink
Log in

An Application of RST as Automated Valuation Methodology to Commercial Properties. A Case in Bari

  • Chapter
  • First Online:
Advances in Automated Valuation Modeling

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 86))

  • 1057 Accesses

Abstract

The lack or unavailability of data poses one of the greatest obstacles hindering the exploration of real estate market information. Due to the small number of observations (cases), there are limited possibilities of using statistical methods, which are generally based on the assumption of a larger number of cases compared to the data describing them. This work provides an application of Rough Set Theory to a small sample of commercial properties as an Automatic Valuation Method. RST has been proposed as an automatic procedure for small sample (d’Amato 2002). It may happen in the application of RST that it is possible to deal with small samples. The rough set theory was applied taking into account the specific nature of information referring to the real property market. This theory is dedicated to examine imprecision, generality and unavailability in the process of data analysis that often occurs on the real estate market. It has been highlighted that one of the problem in AVM application may be the scarcity of data (Downie and Robertson 2007). The model allows the opportunity to reach the results of a single point estimate using if then rules providing a causal non deterministic relationship between price and property characteristics. The model has been applied to a small sample of commercial properties in the city of Bari.

The paper has been written in strict cooperation between the two authors. Therefore the credit of the article should be equally divided between them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bello, R., & Verdegay, L. (2012). Rough sets in the Soft Computing environment. Information Sciences, 212, 1–14.

    Article  MathSciNet  Google Scholar 

  • Borst, R. A., Des Rosiers, F., Renigier, M., Kauko, T., & d’Amato, M. (2008). Technical comparison of the methods including formal testing accuracy and other modelling performance using own data sets and multiple regression analysis. In T. Kauko & M. d’Amato (Eds.), Mass appraisal an international perspective for property valuers. Wiley Blackwell.

    Google Scholar 

  • Chi, D., Yeh, C., & Lai, M. (2011). A hybrid approach of dea. Rough set theory and random forests for credit rating. International journal of innovative computing information and control, 7(8), 4885–4897.

    Google Scholar 

  • Chung, W., & Tseng, T. (2012). Discovering business intelligence from online product reviews: A rule-induction framework. Expert Systems with Applications, 39(15), 11870–11879.

    Article  Google Scholar 

  • d’Amato, M. (2002). Appraising properties with rough set theory. Journal of Property Investment and Finance, 20(4), 406–418.

    Google Scholar 

  • d’Amato, M. (2004). A comparison between RST and MRA for mass appraisal purposes. A case in Bari. International Journal of Strategic Property Management, 8, 205–217.

    Google Scholar 

  • d’Amato, M. (2007). Comparing rough set with multiple regression analysis as automated valuation methodologies. International Real Estate Review, 10(2), 42–64.

    Google Scholar 

  • d’Amato, M. (2008). Rough set theory as property valuation methodology: The whole story. In T. Kauko & M. d’Amato (Eds.), Mass Appraisal an International Perspective for Property Valuers (Chap. 11, pp. 220–258). Wiley Blackwell.

    Google Scholar 

  • d’Amato, M. (2010). A location value response surface model for mass appraising: An “iterative” location adjustment factor in Bari, Italy. International Journal of Strategic Property Management, 14(3), 231–244.

    Google Scholar 

  • d’Amato, M. (2015). Cyclical capitalization. International Journal of Strategic Property Management, 19.

    Google Scholar 

  • d’Amato, M. (2017a). Aspects of commercial property valuation and regressed DCF. In D. Lorenz (Ed.), Behind the price: Valuation in a changing environment. Wiley (in print).

    Google Scholar 

  • d’Amato, M. (2017b). Cyclical capitalization. In D. Lorenz & T. Lutzkendorf (2014) (Eds.), Beyond the price: Valuation in a changing environment. Wiley Publishers, forthcoming.

    Google Scholar 

  • d’Amato, M., & Anghel, I. (2012). Regressed DCF, real estate value, discount rate and risk premium estimation. A case in Bucharest, Aestimum, in print.

    Google Scholar 

  • d’Amato, M., & Kauko, T. (2008). Property market classification and mass appraisal methodology. In T. Kauko & M. d’Amato (Eds.), Mass appraisal an international perspective for property valuers (Chap. 13, pp. 280–303). Wiley Blackwell.

    Google Scholar 

  • d’Amato, M., & Kauko, T. (2011). International encyclopedia of housing and home. In S. J. Smith & M. Elsinga (Eds.), Ong Seow Eng, Susan Watcher e Lorna Fox O’Mahoney. Elsevier Publisher.

    Google Scholar 

  • d’Amato, M., & Kauko, T. (2012). Sustainability and risk premium estimation in property valuation and assessment of worth. Building Research and Information, 40(2), March-April, 174–185.

    Google Scholar 

  • d’Amato, M., & Siniak, N. (2008). Using fuzzy numbers in mass appraisal: The case of belorussian property market. In T. Kauko & M. d’Amato (Eds.), Mass appraisal an international perspective for property valuers (Chap. 5, pp. 91–107), Wiley Blackwell.

    Google Scholar 

  • Dawidowicz, A., Renigier-Bi3ozor, M. & Radzewicz, A. (2014). An algorithm for the purposes of determining the real estate markets efficiency in Land Administration System. Survey Review, 46(36) (May 2014), 189–204. doi:10.1179/1752270613Y.0000000080.                                                                                                                                            

  • Downie, M. L., & Robson, G. (2007). Automated valuation models: an international perspective, Council of Mortgage Lenders, London (pp.10–11). ISBN 1-905257-12-0.

    Google Scholar 

  • IAAO (1999). Standard on ratio studies, Chicago, IL.

    Google Scholar 

  • Kaklauskas, A., Daniūnas, A., Dilanthi, A., Vilius U., Lill, I., Gudauskas, R., et al. (2012). Life cycle process model of a market-oriented and student centered higher education. International Journal of Strategic Property Management, 16, 4, 414–430.

    Google Scholar 

  • Kauko, T., & d’Amato, M. (2008a). Introduction: Suitability issues in mass appraisal methodology. In T. Kauko & M. d’Amato (Eds.), Mass Appraisal an International Perspective for Property Valuers (pp. 1–24). Wiley Blackwell.

    Google Scholar 

  • Kauko, T., & d’Amato, M. (2008b). Preface. In T. Kauko & M. d’Amato (Eds.), Mass appraisal an international perspective for property valuers (p. 1). Wiley Blackwell.

    Google Scholar 

  • Pawlak, Z. (1982). Rough sets. International Journal of Information and Computer Sciences, 11, 341–356.

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak, Z. (1991). Rough sets. Theoretical Aspects of Reasoning about Data: Kluwer Academic Publisher, Dordecht.

    Book  MATH  Google Scholar 

  • Polkowski, L. (2010). Reductive reasoning rough and fuzzy sets as frameworks for reductive reasoning. Approximate reasoning by parts: An introduction to rough mereology. Book Series: Intelligent Systems Reference Library, 20, 145–190.

    Google Scholar 

  • Renigier-Biłozor, M. (2010). Supplementing incomplete databases on the real estate market with the use of the rough set theory. Acta Scientiarum Polonorum, Administratio Locorum, 9(3), 2010, 107–115.

    Google Scholar 

  • Renigier-Biłozor, M. (2011). Analysis of real estate markets with the use of the rough set theory. Journal of the Polish Real Estate Scientific Society, 19(3), 107–118.

    Google Scholar 

  • Renigier-Biłozor, M., & Wiśniewski, R. (2011). The efficiency of selected real estate markets in Poland. Acta Scientiarum Polonorum, Oeconomia, 10(1), 83–96.

    Google Scholar 

  • Renigier-Biłozor, M., & Wiśniewski, R. (2013). The impact of macroeconomic factors on residential property prices indices in europe. Folia Oeconomica Stetinensia Szczecin, 12(2), 103–125. doi:10.2478/v10031-012-0036-3.

    Google Scholar 

  • Renigier-Biłozor, M., Wiśniewski, R., Biłozor, A., & Kaklauskas, A. (2014). Rating methodology for real estate markets—Poland case study. International Journal of Strategic Property Management, 18(2), 198–212. doi:10.3846/1648715X.2014.927401.

    Article  Google Scholar 

  • Stefanowski, J., & Tsoukias, A. (2000). Valued tolerance and decision rules. In Rough Sets and Current Trends in Computing, Vol. 2005 of the series Lecture Notes in Computer Sciences pp. 212–219.

    Google Scholar 

  • Wang, G., & Guan, L. (2012) Data-driven valued tolerance relation. In Li et al. (Eds.), 7th International Conference in China: Rough Sets and Knowledge Technology. RSKT 2012 (pp. 11–19). LNAI 7414. doi:10.1007/978-3-642-31900-6.

  • Zavadskas, E., & Turskis, Z. (2011). Multiple criteria decision making (mcdm) methods in economics: an overview. Technological and Economic Development of Economy, 17(2), 397–427.

    Google Scholar 

  • Zhang, Z. (2012). A rough set approach to intuitionistic fuzzy soft set based decision making. Applied Mathematical Modelling, 36(10), 4605–4633.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DICATECh Technical University Politecnico Di, Bari, Italy

    Maurizio d’Amato

  2. Department of Real Estate Management and Regional Development, University of Warmia and Mazury in Olsztyn, Poland, Faculty of Geodesy, Geospatial and Civil Engineering, Olsztyn, Poland

    Malgorzata Renigier-Biłozor

Authors
  1. Maurizio d’Amato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malgorzata Renigier-Biłozor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maurizio d’Amato .

Editor information

Editors and Affiliations

  1. Property Valuation & Investment DICATECh, Technical University Politecnico di Bari, Bari, Italy

    Maurizio d'Amato

  2. School of the Built Environment, Oxford Brookes University, Headington, Oxfordshire, United Kingdom

    Tom Kauko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

d’Amato, M., Renigier-Biłozor, M. (2017). An Application of RST as Automated Valuation Methodology to Commercial Properties. A Case in Bari. In: d'Amato, M., Kauko, T. (eds) Advances in Automated Valuation Modeling. Studies in Systems, Decision and Control, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-49746-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49746-4_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49744-0

  • Online ISBN: 978-3-319-49746-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation