Expert systems and mass appraisal
| DOI | https://doi.org/10.1108/14635781111150385 |
| Published date | 12 July 2011 |
| Date | 12 July 2011 |
| Pages | 529-550 |
| Author | John Kilpatrick |
| Subject Matter | Property management & built environment |
Expert systems and mass
appraisal
John Kilpatrick
Greenfield Advisors LLC, Seattle, Washington, USA
Abstract
Purpose – The purpose of this paper is to examine the usefulness of a heuristic expert system, to
show its applicability to real-world valuation problems, and to suggest several avenues for statistical
testing.
Design/methodology/approach – The expert systems follow a traditional sales adjustment grid
format, with sufficient data for non-parametric testing.
Findings – The paper finds that, while non-parametric statistics provide weaker results than
traditional (e.g. hedonic regression) modeling, the technique provides a statistically testable model
useful in situations with limited data and/or poorly characterized probability functions.
Practical implications – This paper addresses the conundrum faced by real estate valuers on the
lack of statistical underpinnings of traditional heuristic models.
Originality/value – This is one of the first empirical studies in the valuation literature exploring
statistical characterization of heuristic valuation methods.
Keywords Expert system,Property values, Sales adjustmentgrid, Non-parametricstatistics, Property,
Fair value, Salesmanagement, United States of America
Paper type Research paper
1. Introduction
The purpose of this paper is to explore what is known about expert systems, a set of
methodologies within the category of real estate appraisal sales comparison
approaches which combines the heuristic characteristics of the sales adjustment grid
with some of the statistical power of regression modeling. Expert systems are useful
when the appraiser is confronted with small data sets or the likelihood of
non-normality in values, but nonetheless has a sufficiently large array of data to at
least take advantage of some non-parametric statistical characterizations. Regression
relies on the appraiser’s judgment in the modeling process, but lets the data essentially
speak for themselves in the adjustment phase. Expert systems rely on appraiser
judgment at both levels; but by applying a larger set of data than can be comfortably
managed with a sales grid, this method allows for a heightened degree of accuracy,
reliability, and replicability in the process. Expert systems draw from Bayesian
estimation, and constitute a maximum likelihood estimator of value, which results in
the same coefficients as the least squares estimator derived from a hedonic model, but
approaching the problem from a different perspective
Real estate occupies a unique place on the asset spectrum. The real estate market is
notoriously inefficient, and unlike securities markets, which provide severe penalties
for taking advantage of certain kinds of inefficiencies (e.g. insider trading), real estate
markets actually foster and encourage participants to in this regard. Transactions
require substantial intermediation, high degrees of leverage, and lengthy clearing
periods. The assets themselves suffer from locational monopoly and high degrees of
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-578X.htm
Expert systems
and mass
appraisal
529
Received December 2010
Accepted March 2011
Journal of Property Investment &
Finance
Vol. 29 No. 4/5, 2011
pp. 529-550
qEmerald Group Publishing Limited
1463-578X
DOI 10.1108/14635781111150385
both temporal and spatial autocorrelation. (See, for example, Pace et al., 1998, and Des
Rosiers et al., 2000)
As such, raw asset prices themselves reveal very little about the true value of real
estate; yet an understanding of the actual underlying value is critical for a number of
reasons, including business decisions (particularly financing), forced acquisition
litigation (either through eminent domain or through trespass, such as encroachment
or contamination), and taxation (e.g. – property, estate). This has given rise to a rather
stylized appraisal process.
In the USA, and in most other countries, appraisal methods have developed
heuristically over the years[1]. We can trace appraisal methods and standards in the
USA back to the Virginia House of Burgesses in the 1600s, at which time they gave
instructions on the assessment of property for tax purposes. Profes sional appraisal
organizations arose in the USA in the 1930s, amid a clamor for better organization of
financial markets in general. Various professional appraisal organizations came
together in the 1980s to codify the Uniform Standards of Professional Appraisal
Practice (USPAP), which were then transferred to the newly-formed Appraisal
Foundation, which was empowered via the 1989 Financial Institutions Reform,
Recovery, and Enforcement Act to promulgate both appraisal standards and
qualifications for state-based licensure.
Appraisal methods – as distinct from appraisal standards – continued to be
developed heuristically. The “body of knowledge” evolved on basically a two-track
system, with academic researchers exploring values via statistically robust methods,
such as regression, contingent valuation, or time-series modeling, and practitioners
relying more heavily on professional organizations for methodological guidance. Of
course, in practice, this sort of bi-modality was not so clearly defined, as many
practitioners also held academic appointments, and many academics contributed to
practitioner texts and coursework. Nonetheless, for good or bad, the appraisal
profession did not follow the same path carved out by accountants, who have a more
well-developed integration of academia and accounting practitioners.
In recent years, two separate evolutions have given the profession some pause.
First, arguably, the three largest uses of appraisals in the USA are for property tax
purposes, mortgage financing, and eminent domain “takings”. The first of these is
more-or-less governed by supplemental standards and methods promulgated by the
International Association of Assessing Officers (IAAO). While the IAAO is nominally
a part of the USPAP universe, tax assessors usually adhere to a mass appraisal
paradigm (provided for by USPAP Standard 6), which, in its best applications, closely
resembles hedonic regression. They generally are required to adhere to a certain degree
of statistical rigor, and the IAAO actually promulgates minimum confidence levels,
such as maximum acceptable coefficients of dispersion. Second, mortgage finance
appraisal, however, has no such published thresholds, and that segment of the
profession has been roiled with accusations of inaccuracies, inarguably contributing to
the overall problems with residential mortgage finance today. A thorough examination
of these problems is beyond the scope of this study, but it is sufficient to say that as of
this writing, that segment of the appraisal profession is currently casting abou t for a
better way to do things. To follow-up on Shiller and Weiss (1999), mortgage lending
has apparently recently erred on the side of minimizing Type I errors (failure to make a
deserved loan) but at the expense of increased Type II errors (making loans that should
JPIF
29,4/5
530
To continue reading
Request your trial