Automated valuation models for real estate portfolios. A method for the value updates of the property assets
| Published date | 02 July 2018 |
| Date | 02 July 2018 |
| Pages | 324-347 |
| DOI | https://doi.org/10.1108/JPIF-10-2017-0067 |
| Author | Francesco Tajani,Pierluigi Morano,Klimis Ntalianis |
| Subject Matter | Property management & built environment,Real estate & property,Property valuation & finance |
Automated valuation models for
real estate portfolios
A method for the value updates of the
property assets
Francesco Tajani and Pierluigi Morano
Department of Civil Engineering Sciences and Architecture,
Polytechnic University of Bari, Bari, Italy, and
Klimis Ntalianis
Department of Marketing, Athens University of Applied Sciences, Athens, Greece
Abstract
Purpose –As regards the assessment of the market values of properties that compose real estate portfolios,
the purpose of this paper is to propose and test an automated valuation model. In particular, the method
defined allows for providing for objective, reliable and “quick”valuations of the assets in the phases of
periodic reviews of the property values.
Design/methodology/approach –Aiming at both predictive and interpretative purposes, the method,
based on multi-objective genetic algorithms to search those model expressions that simultaneously maximize
the accuracy of the data and the parsimony of the mathematical functions, is appliedto a sample data of office
properties characterized by medium and large size, located in the city of Milan (Italy) and sold in the period
between 2004 and 2015.
Findings –The model obtained could be an integration of the canonical methodologies (market approach,
income approach, cost approach) implemented in the assessment of the market values of properties, so as to
provide an additional tool to verify the results. In particular, the inclusion of economic variablesin the model
is consistent with the need to reiterate the valuations, contextualizing them to the locational characteristics
and to the current property cycle phase in the specific area.
Practical implications –The model can be applied by all the operators involved in the periodic reviews
of the values of property portfolios: from real estate funds’insiders, in order to monitor the values obtained
through the canonical approaches, to the public institutions, such as the revenue agencies, in order to ensure
the fair payment of the taxes through the updating values of the properties according to the actual and
current market trends.
Originality/value –The method proposed can be a valid support for all public and private entities that hold
significant property assets and that, for various reasons (periodic reviews of the balance sheets, sales,
enhancement, investment,etc.), requirecyclical updatedvalues of the properties. The automated valuationmodel
developed can be used for the assessment of “comparison”values withthe estimates values obtained by other
assessment techniques, in order to ensure a further monitoring tool of the results from the subjects involved.
Keywords Market value, Real estate, Mass appraisal, Genetic algorithms, Automated valuation model,
Market rent
Paper type Research paper
1. Introduction
In the last decade, an unpredictable volatility has involved the real estate market. A recent
study of PricewaterhouseCoopers and Urban Land Institute (2015) reports that over
60 percent of the market operators consulted believe that, in many cases, the properties are
overpriced compared to their real market value. Several operators, in particular, recognize
the risk of a new real estate bubble (Ubs, 2016). According to these experts, after the
numerous corrective actions in real estate sector following the economic crisis generated by
Journal of Property Investment &
Finance
Vol. 36 No. 4, 2018
pp. 324-347
© Emerald PublishingLimited
1463-578X
DOI 10.1108/JPIF-10-2017-0067
Received 3 October 2017
Revised 28 November 2017
Accepted 29 November 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1463-578X.htm
The authors would like to thank the Observatory of the Real Estate Market and Estimative Services
(Italian Revenue Agency), for its availability to provide the necessary real estate data for the analysis
carried out and explained in the paper. The work is attributed in equal parts to the three authors.
324
JPIF
36,4
the US subprime mortgages, in some key financial centers property values have hugely
increased generating an unreasonable overvaluation, especially in relation to possible
sudden changes in demand and/or interest rates.
In the “Pigs”countries (Portugal, Italy, Greece and Spain), the uncertainty of the property
market is structural. In recent years, social, economic and fiscal factors—among these, the
negative trend of the main macroeconomic indicators (GDP reduction, increase in public debt
and unemployment, banking credit crunch)—have produced deep modifications in the property
market that currently appears as a fragile system characterized by continuous transformations.
As regards the credit institutions’loans, despite the numerous efforts of the European
Central Bank, the attitude of the banks remains cautious and is characterized by financial
products that favor borrowers with strong guarantees. The majority of potential buyers,
therefore, are excluded from the possibility of accessing to a funding. Moreover, taxes on
property assets have reached unprecedented levels in a few years. Furthermore, the sale of
public properties, considered as the solution for reducing the national debts and satisfying
the constraints of the European Stability Pact, and the sale of constructions started before
the current economic crisis have been creating an excess supply which can only be absorbed
in the long term. The formation of anomalous prices, the contraction in sales and the
lengthening of the sale time have been the main consequences.
The reforms that have taken place, at international and European level, within the
banking and financial regulation, on the one hand, have increased the capital requirements
to the credit institutions, on the other hand, have made the banking activities more complex,
in particular the traditional activity of financial intermediation. The current regulatory
system is based on the Regulation No. 575/2013 and the Directive No. 2013/36, introduced in
the EU in December 2010, the rules established by the Basel Committee for the banking
supervision, then amended and integrated, in order to implement a system of rules capable
of preventing and dealing with new financial crises.
Another issue concerns the valuation of properties underlying the non-performing loans, for
which the need of property risk assessment tools to estimate prospective values is widespread
shared, in order to allow the credit institutions to identify the highest value of the underlying
guarantees and to transform possible unexpected losses in planned and covered results.
Given the fragmentation of the public databases, the constant opacity of the real estate
market and the uncertainties resulting from the use of asking prices in the valuations,
there is a high need for data-driven approaches, able to correctly use and interpret
accessible data and to correlate them to future socio-demographic and real estate
dynamics. Not by chance, art. 208 (3) (b) of the Capital Requirements Regulation (EU)
No. 575/2013 mentioned above provides the possibility for institutions to “use statistical
methods to monitor the value of the immovable property and to identify the immovable
property that needs revaluation.”
The continuing uncertainty of real estate market causes the peremptoriness of models
which, besides being characterized by a strong theoretical and methodological basis, are
capable of providing for consistently reliable mass appraisals in the short term.
2. Aim
As regards the assessment of the market value of properties that compose real estate
portfolios, in this research an automated valuation model is proposed and tested. The model
is obtained through an innovative method called Evolutionary Polynomial Regression (EPR)
that uses multi-objective genetic algorithms to search those model expressions that
simultaneously maximize the accuracy of the data and the parsimony of the mathematical
functions. In particular, the technique does not require the exogenous definition of the
mathematical expression and the number of parameters that fit better the data collected,
since it is the iterative process of the genetic algorithm that returns the best solution.
325
Real estate
portfolios
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