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[ Pobierz całość w formacie PDF ]Journal of Urban Economics 54 (2003) 474–498
www.elsevier.com/locate/jue
Understanding gentrification: an empirical analysis
of the determinants of urban housing renovation
Andrew C. Helms
Department of Economics, University of Georgia, Athens, GA 30602, USA
Received 19 September 2000; revised 12 June 2003
Abstract
The “back-to-the-city” phenomenon presented an unpredicted countercurrent in the prevalent
tide of suburbanization, and this process of upper-income resettlement in the inner city has been
thoroughly analyzed in the urban economic literature. Housing renovation, a process that always
accompanies gentrification and constitutes a significant portion of residential housing investment,
has been studied much less. Contrary to the expectation that “location matters,” the existing empirical
studies have concluded that most neighborhood amenities and structural attributes are insignificant
as determinants of renovation. Using a detailed parcel-level data set that documents all residential
renovation activity in Chicago between 1995 and 2000, this paper establishes that the characteristics
of a building and its neighborhood do indeed influence the likelihood that it will be renovated.
2003 Published by Elsevier Inc.
1. Introduction
Surprisingly and encouragingly, recent data from the 2000 Census [32] have revealed
that after a half-century of continual population loss, the cities of New York and Chicago
gained residents between 1990 and 2000. Some demographers dismally maintain that the
scales were tipped by high birth rates (especially among inner-city immigrants), not by the
widespread adoption of pro-urbanist preferences among consumers. At best, the population
growth is the result of a gradual but steady shift in residential behavior patterns.
A countercurrent in the tide of suburbanization was first detected in the late 1960s: some
inner-city neighborhoods were unexpectedly being resettled by middle- and upper-income
E-mail address:
ahelms@terry.uga.edu.
0094-1190/$ – see front matter 2003 Published by Elsevier Inc.
doi:10.1016/S0094-1190(03)00081-0
A. Helms / Journal of Urban Economics 54 (2003) 474–498
475
“pioneers,” who were typically young, childless, and well educated.
1
Gentrification, as
the phenomenon was dubbed, garnered breathless media coverage, attracted academic
attention, and raised the hopes of city governments. Though gentrification did not herald
the end of suburbanization, neither was it a transitory trend. It has steadily persisted, if
not gathered momentum, over the past three decades. During this time, gentrification has
revealed itself to be less often a one-way migration back to the city than a continual
circulation through the city: as one demographer straightforwardly explained (about
Chicago), “You’ve got all these 20-year-olds coming in, and all these 30-year-olds going
out.”
2
In addition, gentrifiers include the so-called “empty-nesters,” who return to the city
and stay throughout the second childless phase of their lives.
Even though the city often loses the younger cohort of (re)settlers to the suburbs after
they start families, it retains the physical improvements that they made to their residences,
and also benefits from the upgrading investments of the returning empty-nesters. Housing
rehabilitation, which is certainly the most visible evidence of gentrification, improves the
city’s physical health by forestalling further decay of the housing stock and improves
its fiscal health by boosting the property tax base. The sheer volume of expenditures on
residential improvements is notable: in the year 2000, when US households spent $160
billion on construction of new single-family homes, owner-occupiers of existing single-
family homes spent
more than half
that amount ($81 billion) on home improvements,
not including routine maintenance and repair.
3
In cities, the ratio is even more striking:
in Chicago between 1995 and 2000 (the city and time period analyzed in this paper),
investment in new construction and investment in the improvement of existing housing
were
nearly equal
.
4
Of course, not all inner-city renovation activity is gentrification-based; much of it is per-
formed by existing city residents.
5
This “incumbent upgrading” is a relatively predictable
and continual occurrence in historically stable areas. While its effects are certainly not
negligible, they are usually gradual and often nearly invisible. By definition, incumbent
upgrading does not significantly alter the demographic or socioeconomic composition of
a neighborhood. Consequently, it does not dramatically change neighborhoods, let alone
catalyze city-wide revitalization. Though gentrification is also unlikely to singlehandedly
revitalize America’s inner cities, it does markedly transform neighborhoods, both phys-
1
The preferences and demographics of the “first-generation” gentrifiers are thoroughly documented by
Kern [17] and by many sociologists (e.g., Clay [11] and Gale [13]). It is generally agreed that the personal
characteristics of their current counterparts are quite similar.
2
Ken Johnson in P. Reardon, “Floating in Data and Loving it,”
Chicago Tribune
, Nov. 8, 2001.
3
The Census Bureau separates “residential improvements and repairs” into two categories: “maintenance and
repairs,” and “improvements.” For the purposes of this paper, the terms
renovation, rehabilitation
,and
alteration
are considered synonymous with the latter category [31].
4
The valuation/construction cost of new construction between 1/1/96 and 12/31/00 was $1.84 billion (US
Census Bureau [31]); expenditures on renovations, excluding additions, between 10/19/95 and 10/25/00 were
$1.75 billion (Chicago Department of Buildings [8]).
5
As a very rough approximation, about 56% of the renovations performed in Chicago between 1995 and
2000 (the sample analyzed by this paper) was “incumbent upgrading”: on average, renovation activity occurred
in neighborhoods in which 44% of the residents were recent in-movers, according to block-level Census data
from 1990 [30].
476
A. Helms / Journal of Urban Economics 54 (2003) 474–498
ically and demographically (with some side-effects, as sociologists have noted).
6
As a
result, the housing renovation that accompanies gentrification is a process that is important
to understand. However, there exist only a few empirical studies of residential renova-
tion, and none of them provides a rigorous and conclusive answer to the central questions:
What exactly are the determinants of urban housing renovation? Which local amenities and
structural characteristics attract renovators to certain neighborhoods?
Some sociologists have hypothesized answers to these questions in their case studies
of gentrified neighborhoods.
7
From this literature, the mainstream press, and even casual
observation, the common characteristics of gentrified areas are easily identifiable. Most
of the neighborhoods consist of historic, low-density, architecturally distinctive houses,
and they frequently feature parks and pleasant views. They are usually quite proximate to
the central business district (CBD) and convenient to mass transit, and they are almost
always far away from highways, public housing projects, and other disamenities. The
houses in neighborhoods like these can intuitively be expected to experience a high
level of renovation activity. A neighborhood’s demographic characteristics (such as racial
composition, average income, age distribution, and ethnicity) are also likely to affect
gentrification and renovation activity, but the exact nature and extent of their influence
are difficult to conclusively determine from anecdotal analyses.
Most existing empirical studies of renovation either fail to adequately account for
the idiosyncratic attributes of individual buildings and neighborhoods, or find that these
attributes are statistically insignificant predictors. Two exceptions are the studies by
Mayer [20] and Melchert and Naroff [22]. Mayer analyzes renovation activity among
rental properties in Berkeley, California. The results of his logit regression confirm the
expected effects of buildings’ characteristics: older, smaller, owner-occupied units that
were structurally sound (but not necessarily good-looking) and had not been recently
renovated were the most likely to be rehabilitated.
8
However, the effects of many of the
neighborhood characteristics—including noise and traffic levels, non-residential land uses,
population density, and distance from the university campus (Berkeley’s counterpart of a
CBD)—are statistically insignificant. Melchert and Naroff use a wide variety of block-level
Census data to characterize the buildings and neighborhoods in their study of neighborhood
gentrification in Boston. Of the 34 explanatory variables that they consider, five—distance
to downtown, proximity to a small or medium-sized open area, pre-1900 construction,
and average rent in each block—have statistically significant coefficients in their final
regression.
9
6
The essays collected by Laska and Spain [18] discuss a variety of these issues. One of the most serious and
commonly-cited side effects, the displacement of lower-income residents, is thoroughly analyzed by Nelson [27].
7
See Clay [11] and Laska and Spain [18].
8
Mayer’s results establish that these characteristics influence landlords’ decisions to renovate rental housing,
but there is no evidence (nor claim) that they similarly influence homeowners’ renovation decisions. However,
the positive coefficient of owner-occupancy suggests that non-absentee landlords might perform more renovations
because they “can tailor improvements on their own dwelling units to their own tastes” (p. 85). If this hypothesis
is correct, then the results of Mayer’s study—in which 92% of the buildings in the sample were owner-occupied—
are indeed relevant.
9
Melchert and Naroff’s dependent variable is an appraisal by the Boston Redevelopment Authority of
whether or not each block had experienced gentrification. While this approach has the advantage of separating
A. Helms / Journal of Urban Economics 54 (2003) 474–498
477
From the rest of the empirical literature, only one result consistently emerges: the
likelihood of renovation increases with a building’s age. Mendelsohn’s [23] study, one of
the earliest empirical examinations of renovation, includes no building or neighborhood
characteristics other than age. Shear [28] uses American Housing Survey (AHS) data
to examine a household’s decision to move or to stay and renovate. Of the building
attributes that he includes as explanatory variables, only age and a dummy variable for
structural soundness are significant. Montgomery [25] also analyzes the move vs. renovate
decision using AHS data. Of the seven dwelling and neighborhood characteristics that she
includes, building age is the only variable that has a statistically significant influence on the
likelihood that a household will improve its property.
10
AHS data are also used by Bogdon
[6], who focuses on a homeowner’s decision to hire outside help for renovations. In her
regressions, age and square footage are the only significant building attributes, and none of
the neighborhood attributes is significant. Galster [14] gathers and analyzes detailed survey
data on housing “upkeep.” In his results, the significant predictors include many of the
homeowners’ characteristics but only one of the building and neighborhood characteristics
(again, building age). Likewise, Chinloy [10] includes 14 housing characteristics in his
estimation of maintenance expenditures (excluding improvements), but only building age
turns out to be a significant explanatory variable.
In spite of these results, this paper rejects the apparent conclusion that all structural
characteristics, neighborhood attributes, and local amenities except building age are
insignificant determinants of residential renovation. Empirically, the effects of 27 of these
variables (most of which are measured at the parcel or block level) are definitively
established by analyzing a set of microdata that documents all renovation activity among
Chicago buildings over the years 1995 to 2000. The estimation results confirm that building
age does indeed have a significant influence on improvement activity. Additionally, seven
variables establish the effects of housing density and vacancy (both at the building and
neighborhood level) on the likelihood of renovation. Accessibility to the CBD, measured
by three variables, and three neighborhood (dis)amenities also have significant effects.
Finally, most of the demographic variables, which describe the racial composition, average
income level, and age distribution of each neighborhood, have significant coefficients as
well.
In Section 2 of this paper, the theoretical literature that is relevant to gentrification
and renovation is summarized, and a simple model of household renovation behavior is
developed. In Section 3, the data are described and hypotheses about the variables are
discussed. Empirical results are presented and reviewed in Section 4. Finally, Section 5
discusses the implications and conclusions of the analysis.
gentrification activity from incumbent upgrading, it relies upon a subjectively-determined dummy variable instead
of “hard” data on actual consumer behavior.
10
When the dependent variable includes routine maintenance in addition to improvements, four of the seven
dwelling and neighborhood variables have significant coefficients. Six of the seven coefficients are significant
when the dependent variable is the dollar amount of improvement expenditures (instead of a dummy variable
indicating the occurrence of improvement activity).
478
A. Helms / Journal of Urban Economics 54 (2003) 474–498
2. Theory
2.1. Gentrification as a prediction of the urban model
Gentrification encompasses the two distinct processes of upper-income resettlement and
housing renovation, which are usually modeled separately as independent phenomena.
While this paper is concerned with the latter, studies of spatial income patterns can
indirectly provide insight into the process of housing renovation. The models that explicitly
include urban amenities are particularly relevant to this paper.
Early models of a monocentric city by Alonso [1], Mills [24], and Muth [26] predict a
spatial equilibrium in which income increases with distance from the center. This outcome
relies on the assumption that housing demand is more income-elastic than commuting
costs. Wheaton [33] empirically tests this assumption and finds that the two income
elasticities are very similar. Consequently, the bid-price functions are almost identical
across income groups, making the model’s income segregation predictions “statistically
unreliable” (p. 631). This conclusion lends credence to the suspicion that urban spatial
income patterns, including the upper-income resettlement component of gentrification, are
strongly influenced by factors that are omitted from the simple urban model.
By focusing on changes in transport mode choice, LeRoy and Sonstelie [19] attempt
to explain the spatial income patterns of three distinct phases in the life cycle of a
city: “paradise,” when the rich live downtown; “paradise lost,” when the rich flee to the
suburbs; and “paradise regained,” when they resettle downtown. To capture the effects
of transportation innovations, they extend the Alonso–Muth model to include a bimodal
choice of transit. As income growth occurs and commuting costs vary, mode-switching
may occur differentially across income groups (e.g., the rich adopting streetcars while the
poor continue to walk to work). This switching can lead to location reversals and generate
spatial equilibria that mirror all three phases above. However, LeRoy and Sonstelie’s
empirical results support only the first two phases: data from the early 20th century (before
automobiles became widely affordable) uphold the model’s predictions, but data from the
1950s–1970s yield inconclusive results. These results suggest that gentrification, unlike
earlier shifts in residential location patterns, is not a simple consequence of transportation
innovation.
By introducing locational amenities into the Alonso–Muth framework, the models of
Berry and Bednarz [5] and Brueckner et al. [7] help explain upper-income resettlement.
The equilibrium location pattern is determined not only by the relative income elasticities
of housing demand and commuting costs, as in the standard model, but also by the slope
of the amenity gradient and the rate at which consumers’ marginal valuation of amenities
rises with income. If the central city has a strong and growing amenity advantage over
the suburbs and amenity valuation is highly income-elastic, then the rich will (re)locate
downtown.
Kern [15,16] additionally assumes that some goods and services are obtainable only
at the city center, in contrast to the standard composite consumption good that can be
purchased anywhere in the city. To consume these goods, which represent urban amenities
such as cultural, social, and entertainment activities, residents must make extra trips
downtown in addition to their regular commutes. By also including a taste parameter
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