Access the full text.
Sign up today, get DeepDyve free for 14 days.
E. Jahanshiri, T. Buyong, A. Shariff (2011)
A review of property mass valuation models
A. Nagar, N. Kakwani (1964)
THE BIAS AND MOMENT MATRIX OF A MIXED REGRESSION ESTIMATOREconometrica, 32
T. Fik, David Ling, G. Mulligan (2003)
Modeling Spatial Variation in Housing Prices: A Variable Interaction ApproachReal Estate Economics, 31
Jozef Zurada, Alan Levitan, Jian Guan (2011)
A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal ContextJournal of Real Estate Research, 33
S. Gnat (2020)
Analysis of the Impact of the Type of Sampling of Representative Properties on the Results of Mass AppraisalFolia Oeconomica Stetinensia, 20
N. Crosby, J. Henneberry (2016)
Financialisation, the valuation of investment property and the urban built environment in the UKUrban Studies, 53
L. Anselin (1998)
GIS Research Infrastructure for Spatial Analysis of Real Estate MarketsJournal of Housing Research, 9
H. Theil, A. Goldberger (1961)
On Pure and Mixed Statistical Estimation in EconomicsInternational Economic Review, 2
W. Mccluskey, M. McCord, P. Davis, M. Haran, David McIlhatton (2013)
Prediction accuracy in mass appraisal: a comparison of modern approachesJournal of Property Research, 30
J. Durbin (1953)
A Note on Regression When There is Extraneous Information About One of the CoefficientsJournal of the American Statistical Association, 48
J. Kilpatrick (2011)
Expert systems and mass appraisalJournal of Property Investment & Finance, 29
Martin Hoesli, Steven Bourassa, E. Cantoni (2010)
Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods
H. Theil (1974)
Mixed Estimation Based on Quasi-Prior JudgmentsEuropean Economic Review, 5
Yu Zhou, D. Haurin (2010)
On the Determinants of House Value VolatilityJournal of Real Estate Research, 32
E. Pagourtzi, V. Assimakopoulos, T. Hatzichristos, N. French (2003)
Real estate appraisal: a review of valuation methodsJournal of Property Investment & Finance, 21
M. Doszyń (2020)
Algorithm of real estate mass appraisal with inequality restricted least squares (IRLS) estimationJournal of European Real Estate Research, 13
P. Swamy, J. Mehta (1969)
On Theil's Mixed Regression EstimatorJournal of the American Statistical Association, 64
M. d'Amato (2017)
A Brief Outline of AVM Models and Standards Evolutions, 86
C. Liew (1976)
Inequality Constrained Least-Squares EstimationJournal of the American Statistical Association, 71
R. Mittelhammer, R. Conway (1988)
Applying Mixed Estimation in Econometric ResearchAmerican Journal of Agricultural Economics, 70
R. Pace, Otis Gilley (1990)
Estimation employing a priori information within mass appraisal and hedonic pricing modelsThe Journal of Real Estate Finance and Economics, 3
A. Korkhin (2013)
Using a priori information in regression analysisCybernetics and Systems Analysis, 49
G. Judge, T. Takayama (1966)
Inequality Restrictions in Regression AnalysisJournal of the American Statistical Association, 61
M. Lovell, E. Prescott (1970)
Multiple Regression with Inequality Constraints: Pretesting Bias, Hypothesis Testing and EfficiencyJournal of the American Statistical Association, 65
F. Wolak (1989)
Testing inequality constraints in linear econometric modelsJournal of Econometrics, 41
H. Theil (1963)
On the Use of Incomplete Prior Information in Regression AnalysisJournal of the American Statistical Association, 58
S. Gnat, M. Doszyń (2020)
Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets*European Research Studies Journal
J. Mehta, P. Swamy (1970)
The Finite Sample Distribution of Theil's Mixed Regression Estimator and a Related Problem, 38
The purpose of this paper is to present how prior knowledge about the impact of real estate features on value might be utilised in the econometric models of real estate appraisal. In these models, price is a dependent variable and real estate features are explanatory variables. Moreover, these kinds of models might support individual and mass appraisals.Design/methodology/approachA mixed estimation procedure was discussed in the research. It enables using sample and prior information in an estimation process. Prior information was provided by real estate experts in the form of parameter intervals. Also, sample information about the prices and features of undeveloped land for low-residential purposes was used. Then, mixed estimation results were compared with ordinary least squares (OLS) outcomes. Finally, the estimated econometric models were assessed with regard to both formal criteria and valuation accuracy.FindingsThe OLS results were unacceptable, mostly because of the low quality of the database, which is often the case on local, undeveloped real estate markets. The mixed results are much more consistent with formal expectations and the real estate valuations are also better for a mixed model. In a mixed model, the impact of each real estate feature could be estimated, even if there is no variability in the sample information. Valuations are also more precise in terms of their consistency with market prices. The mean error (ME) and mean absolute percentage error (MAPE) are lower for a mixed model.Originality/valueThe crucial problem in econometric property valuation is that it involves the unreliability of databases, especially on undeveloped, local markets. The applied mixed estimation procedure might support sample information with prior knowledge, in the form of stochastic restrictions imposed on parameters. Thus, that kind of knowledge might be obtained from real estate experts, practitioners, etc.
Journal of European Real Estate Research – Emerald Publishing
Published: Oct 20, 2021
Keywords: Econometric real estate appraisal; Mixed estimation; Prior information; Theil–Goldberger estimator
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.