Search the Journal of Real Estate Research
rental cost analysis
Matching apartment features and renter characteristics to determine
the optimal rental price.
Utility Theory and Rent Optimization: Utilizing Cluster Analysis to
Segment Rental Markets
"Abstract: The research reported here segments apartment residents and
units into homogeneous groups. The segmentation is accomplished via
cluster analysis. The purpose of the research is to identify market
segments with higher marginal utility preferences for selected project
or unit amenities; with the ultimate goal of rental rate
The authors of the following articles propose several formulae for
rental price calculation, taking into account a number of variables,
including vacancies, maintenance, etc. References are given in each
article for further reading.
Rental Amenities and the Stability of Hedonic Prices: A Comparative
Analysis of Five Market Segments
François Des Rosiers
"Abstract. The current paper applies the hedonic approach to five
rental submarkets in the Quebec region, namely Quebec City, Vanier,
Ste-Foy, Beauport and Charlesbourg. The databank consists of
information obtained from property owners via a yearly survey; some
32,000 rental units and nearly 3,300 buildings are included in the
study. Data provide detailed information on building and apartment
size, age, location, services provided, quality of premises and type
of occupants; vacancy rates can also be derived from the bank. In
addition, resorting to a regional geographic information system
permits integration of neighborhood effects into the analysis.
Findings suggest that significant differences in implicit prices do
exist across market segments. However, while consistent results are
obtained for major rent determinants, collinearity clearly emerges
with respect to some rental attributes. Using a regression-based
paired comparison approach, it is possible to identify stable hedonic
prices for main rental services; the coefficients thus obtained are
then forced back as constraints into the service-adjusted model,
thereby improving its overall consistency and practicability."
"As shown by both Guntermann and Norrbin (1987) and Sirmans, Sirmans
and Benjamin (1989, 1990), the physical attributes of a rental
property, services delivered as well as access and neighborhood
factors, all play an important role in determining rents, and hence
The Determinants of Rent: A Brief Survey of the Literature
In their extensive survey of the literature on market rent
determinants, Sirmans and Benjamin (1991) have reviewed some thirty
studies performed since 1973 and classified them according to three
categories of rent determinants as identified by authors: these are
property-specific attributes (including locational and socioeconomic
factors), management-specific factors (rental concessions, property
management and length of residency) and, finally, vacancies. Major
findings are summarized here, together with other research conclusions
recently published on the subject.
On Setting Apartment Rental Rates: A Regression-Based Approach
Joseph L. Pagliari, Jr
James R. Webb
"Abstract. This study presents a regression-based analysis of
apartment rents for a cross-section of properties located in an edge
city submarket. It attempts to provide a solution for owners and
managers of apartments to the thorny problem of setting a propertys
rental rate. The approach used in this analysis differs from previous
studies in at least three important respects: (1) vacancy is treated
as part of the dependent variable, (2) the property-specific rental
rate generated by the regression analysis is compared to the
propertys actual effective rent, and (3) each property in the
submarket is ranked by the difference between its actual effective
rent and its characteristic-adjusted effective rent. This is then
followed by several observations concerning the advantages and
disadvantages of such an analysis in a practical setting."
"In this study, regression analysis is used to estimate the
appropriate rental rate for a cross-section of apartment properties.
Regression packages 1 are now available in most electronic spread
sheets. Therefore, owner/operators in the multifamily sector, as with
most business managers, can substantially improve their decisionmaking
processes at very little cost. Previous regression applications to
real estate pricing issues include: (1) the appraisal of single-family
homes (see Blettner, 1969; Case, 1967; Dilmore, 1974, Emerson, 1972;
Rosen and Smith, 1983), (2) the appraisal of multifamily projects (see
Hanford, 1966; Shenkel, 1969; Webb, 1982), (3) estimating demand for
retail space (see Benjamin, Jud and Okoruwa, 1994; Whaley, 1990), (4)
estimating the natural vacancy rate for apartment markets (see Gabriel
and Nothaft, 1988; Harris, 1991; Miles, 1975; Read, 1988) and (5)
estimating the market rents for apartment markets (see Sirmans and
"In order to estimate the appropriate rental rate, a linear multiple
regression analysis of the following form was used:
yia+b1,ix1,i+b2,ix2,i+b3,ix3,i+ei , (1)
yi value of the effective monthly rent,
bn coefficient modifying xn,
x1,i vector of non-dummy quantitative variables,
x2,i vector of dummy quantitative variables,
x3,i vector of dummy qualitative variables, and
ei error term."
Assessing the Rental Value of Residential Properties: An Abductive
Learning Networks Approach
Kee S. Kim
Walt A. Nelson
"Abstract. This paper attempts to estimate rental value of residential
properties using Abductive Learning Networks (ALN), an 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."
"The purpose of this study is to build a model that can provide an
accurate way of assessing the market value of residential rental
property and analyzing the factors that determine market rents by
using an artificial intelligence technique. Our main concern is to
examine whether the Abductive Learning Networks (ALN) technique can be
used to overcome many difficulties associated with the multiple
regression technique which has been used primarily to analyze the
price behavior of rental houses in the current literature (Guntermann,
1987; Murphy, 1989; Meacham, 1988; Jud and Winkler, 1991, Weirick and
Ingram, 1990). The multiple regression technique is parametric and
requires the user to specify the functional form of the solution. If
one does not know or cannot guess the correct underlying form of the
functional relationship, the regression approach will result in
inaccurate models. Although the regression approach allows the use of
a very general polynomial equation when the functional relationship is
likely to be nonlinear, it becomes virtually impossible to estimate
all coefficients, primarily because the number of coefficients to be
estimated grows factorially as the number of variables and degrees of
the function increase."
The following article expresses the values of a rental model in terms
of maintenance costs.
Maintenance of Residential Rental Property: An Empirical Analysis
Thomas M. Springer
Neil G. Waller
"Abstract. The maintenance costs of 137 residential rental properties
in northwestern South Carolina are analyzed. The results show that
maintenance cost per square foot increases with property age, tenant
turnover, certain amenities, and for higher-rent properties. Compared
to other property types, apartments exhibit higher maintenance costs
per square foot with larger complexes showing lower per square foot
maintenance costs than smaller complexes. This cost economy suggests
added value to rental housing for larger complexes. Owners of multiple
properties are found to pay higher maintenance costs. Finally, there
is no observed relationship between absentee ownership and the level
of property maintenance."
"The level of property maintenance and the associated maintenance cost
interrelate with housing quality and rent levels. A higher level of
quality is associated with higher rents and requires more maintenance
activity. Many academic studies of housing depreciation (see, for
example, Dildine and Massey, 1974; Read, 1991) analyze the
relationships between housing quality, rent levels and the level of
property maintenance. Rent is a common proxy for housing quality.
Ceteris paribus, a decline in rent evidences a decline in housing
quality, and vice versa. Factors that affect rent logically affect
housing quality.2 The quality of housing, Q,
Q=f(Co,It,M) , (1)
is a function of Co, physical property characteristics at the time of
construction; It, additional capital investment since construction;
and M, both current and past levels of maintenance activity (Arnott,
Davidson and Pines, 1983). Because Co is variant, changes in Q result
from investment decisions (It) subsequent to the time of construction
and ongoing maintenance activity (M), which affects the physical
deterioration of Co."
Arnott, R., R. Davidson and D. Pines, Housing Quality, Maintenance and
Rehabilitation, Review of Economic Studies, July 1983, 50, 46792.
Dildine, L. L. and F. A. Massey, Dynamic Model of Private Incentives
to Housing Maintenance, Southern Economic Journal, April 1974, 40,
Randolph, W. C., Estimation of Housing Depreciation: Short-Term
Quality Change and Long-Term Vintage Effects. Journal of Urban
Economics, March 1988, 23, 16278.
Read, C., Maintenance, Housing Quality, and Vacancies under Imperfect
Information, Journal of the American Real Estate and Urban Economics
Association, 1991, 19:2, 13853.
Rosenberg, S. M. and J. C. Corgel, Agency Costs in Property Management
Contracts, AREUEA, Journal, 1990, 18:2, 184201.
Sirmans, G. S. and J. D. Benjamin, Determinants of Market Rent,
Journal of Real Estate Research, Fall 1991, 6:3, 35779.
Solt, M. E. and N. G. Miller, Managerial Incentives: Implications for
the Financial Performance of Real Estate Investment Trusts, AREUEA
Journal, Winter 1985, 13:4, 40423.
Vorst, A. C. F., Optimal Housing Maintenance Under Uncertainty,
Journal of Urban Economics, May 1987, 21, 20927.
Wang, K., T. V. Grissom, J. R. Webb, and L. Spellman, The Impact of
Rental Properties on the Value of Single-Family Residences, Journal of
Urban Economics, September 1991, 30, 15266.
White, H., A Heteroskedasticity-Consistent Covariance Matrix Estimate
and A Direct Test for Heteroskedasticity, Econometrica, May 1980, 48,
Links from the Journal of Real Estate Research
An analysis of rental trends and factors affecting them.
Snohomish County Tomorrow 2001 Growth Monitoring Report
Rental Housing Market: Price Report and Affordability Analysis
Factors in determining market value.
APPRAISAL RESEARCH AND ANALYSIS