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Assessing the Rental Value of Residential Properties: An Abductive Learning Networks Approach

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Abstract

This paper attempts to estimate rental value of residential properties using Abductive Learning Networks (ALN), and artificial intelligence technique. The results indicate that the ALN model provides an accurate estimation of rents with only seven input variables, while other multivariate statistical techniques do not. The ALN model automatically selects the best network structure, node types and coefficients, and therefore it simplifies the maintenance of the model. Once the final model is synthesized, the ALN model becomes very compact, rapidly executable and cost-effective.

Suggested Citation

  • Kee S. Kim & Walt A. Nelson, 1996. "Assessing the Rental Value of Residential Properties: An Abductive Learning Networks Approach," Journal of Real Estate Research, American Real Estate Society, vol. 12(1), pages 63-78.
  • Handle: RePEc:jre:issued:v:12:n:1:1996:p:63-78
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    References listed on IDEAS

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    1. William B. Shear, 1983. "A Note on Occupancy Turnover in Rental Housing Units," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 11(4), pages 525-538.
    2. Smith, Lawrence B & Rosen, Kenneth T & Fallis, George, 1988. "Recent Developments in Economic Models of Housing Markets," Journal of Economic Literature, American Economic Association, vol. 26(1), pages 29-64, March.
    3. Karl L. Guntermann & Stefan Norrbin, 1987. "Explaining the Variability of Apartment Rents," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 15(4), pages 321-340.
    4. Frank W. Porell, 1985. "One Man's Ceiling Is Another Man's Floor: Landlord/Manager Residency and Housing Condition," Land Economics, University of Wisconsin Press, vol. 62(2), pages 106-118.
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    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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