Instructions turn in an essay for each question (approximately 1 .docx

Instructions: turn in an essay for each question (approximately 1
page or more in length) complete with examples. Credit is given
for correct answers but extra points are added for real life
examples that demonstrate understanding of the application of
the concepts from the text. Each question is worth 5 points.
1. In a short essay, explain the meaning of international trade.
Describe the two major forms through which international trade
takes place. Explain the relationship between international trade
and national prosperity and provide examples to illustrate this
relationship.
2. In a short essay, discuss the impact of the Internet on
traditional intermediaries and on retail businesses. Provide
examples of retailers who have embraced the new technology
and discuss how the Internet has affected their retail business.
3. In a short essay, discuss the three cultures into which
employees are socialized — national culture, professional
culture, and corporate culture — and explain why the
differences between these cultures are especially important to
understand for international firms working in the services
sector.
4. In a short essay, explain the four-step framework for making
ethical decisions. Provide an example of how the framework can
help managers resolve ethical dilemmas.
Answer: The four-step framework for making ethical decisions
proceeds as follows.
5. In a short essay, explain how quotas work as an instrument of
government intervention, and include a description of voluntary
export restraints in your answer. How can firms use foreign
trade zones as strategy to manage government intervention?

6. In a short essay, describe the various types of partners an
international firm might adopt when expanding internationally.
How can a business partner affect a company's sales potential?
100
Valuing New Development
in Distressed Urban
Neighborhoods
We estimate the effect of design on the
assessed values of new housing units in
high-poverty Chicago census tracts with
a parcel-based hedonic regression in
which we distinguish between three
urban design types: enclave, traditional
neighborhood development (TND), and
infill. We find that urban design signifi-
cantly affects housing values, and infill
housing is more highly valued than
either enclave or TND housing. We also

examine the influences of individual urban
design features and find that residents
prefer entrances that face the street, and
facades constructed from the same
material as adjacent buildings. They also
prefer parking in front of their homes, and
to be buffered from public streets. We
interpret the former to be preferences for
greater integration into the surrounding
neighbourhood, consistent with our
findings on infill.
Brent D. Ryan, AICP ([email protected]), is
co-director of the City Design Center and
an assistant professor of urban planning
and policy at the University of Illinois at
Chicago. His research interests include
urban design, neighborhood revitaliza-
tion, and morphological change in urban
areas. Rachel Weber ([email protected])

is an associate professor of urban planning
and policy at the University of Illinois at
Chicago. She is the author of numerous
articles, technical reports, and a book in
the fields of development finance, urban
real estate markets, and industrial
restructuring.
Does Design Matter?
Brent D. Ryan and Rachel Weber
housing construction boom occurred in some of the poorest
urban neigh-
borhoods in the United States in the 1990s. Attracted by vacant
land and
new markets, and possessing access to cheap credit, for-profit
developers
built a mix of housing, ranging from multifamily buildings to
gated single-family
homes in poor neighborhoods. The urban design of this new
housing varied
widely.
In this article, we examine whether urban design is a significant
contributor
to the value of new housing in poor urban neighborhoods,
assuming that resident

preferences are revealed in the prices paid for different kinds of
housing and that
these in turn are reflected in their assessed values. We
distinguish between three
urban design types: enclave, traditional neighborhood
development (TND), and
infill. We perform a parcel-based hedonic regression to explain
the values of new
housing constructed in high-poverty Chicago census tracts
between 1993 and
2003. We investigate the relationship between urban design and
housing values
in poor neighborhoods, about which little is known, because
previous research
on the effects of urban design on housing values has focused
almost exclusively
on new urbanist projects in more affluent areas. We also hope to
make local
governments aware of the potential of urban design policies to
create value in
distressed neighborhoods and to reduce resistance to new
development products
among realtors and tax assessors who shape real estate market
practices.
The Urban Design of New Inner-City Housing
Substantial amounts of privately financed housing have been
constructed
in distressed, inner-city neighborhoods during the past decade
(Ryan, 200 6 a),
transforming them through an influx of higher-income residents
(Jargowsky, 2003;
Wyly & Hammel, 1999) and capital. Urban design is

particularly important in
this context. In poor areas with large amounts of vacant land,
developers can
sometimes acquire whole city blocks and reshape street
networks (Ryan, 2006b),
creating more design options than elsewhere. Urban design may
also reduce the
social isolation of low-income households, enhancing their
integration into the
larger urban economy (Duany, Plater-Zyberk, & Speck, 2000;
U. S. Department
of Housing and Urban Development [HUD] 2000; Wilson,
1996).
journal of the American Planning Association,
Vol. 73, No. 1, Winter 2007
© American Planning Association, Chicago, IL.
Ryan and Weber: Valuing New Development in Distressed
Urban Neighborhoods
Studies of distressed neighborhoods in Detroit and
Philadelphia (Ryan, 2002, 2006b), Pittsburgh (Dietrick &
Ellis, 2004), and our preliminary observations in Chicago,
show that new housing development in poor neighborhoods
can be grouped into three urban design types: (1) infill, or
scattered-site, development; (2) traditional neighborhood
development (TND); and (3) enclave, or self-contained, de-
velopment. Table 1 details some characteristics of each type.
Infill development (illustrated in Figure 1) occurs
where small numbers of parcels are available for redevelop-

ment on existing city blocks. This type of development does
not change the neighborhood structure substantially be-
cause new housing is located between existing buildings
oriented to current street and lot subdivision patterns.
TND and enclave developments (examples in Figures 2
and 3) occur where empty parcels are numerous enough
to permit the construction of extensive, contiguous, new
housing. These latter types allow designers much more
flexibility in how they locate housing, open space, roadways,
and parking areas.
TNDs integrate new development into their surround-
ings by replicating the design features of existing neighbor-
hoods, like street-facing housing and interconnected street
grids. TNDs have much in common with new urbanist
designs (Bothwell, Gindroz, & Lang, 1998; Morrow-Jones,
Irwin, & Roe, 2004; Talen, 2001; Steuteville, 1999; Leccese
& McCormick, 2000). In contrast, enclaves reject their
contexts by spatially isolating new housing from their sur-
roundings through the orientation and spatial placement of
buildings and roadways. Bohl (2000) refers to enclave de-
velopments as "inward-focused residential pods" (p. 767).
Urban. Design's Effect on Housing Value
Urban economists have demonstrated that a property's
attributes affect its price in ways that can be measured (see
Boyle & Kiel, 2001; Sirmans, MacPherson, & Zietz, 2005
for literature reviews). Few economists have specifically con-
sidered the design of the built environment (or if they have
it has been in the context of new suburban developments)
even though differences in urban design might affect
housing prices by influencing development costs, amenities,
and uncertainty about future development nearby.

Development Costs
Each of the three types of urban design described
above uses space in a different manner, potentially affecting
construction cost, sale price, and assessed value. Housing
units in enclaves and TNDs are likely to cost less to build
than comparable infill units because they can take advan-
tage of economies of scale (Gyourko & Rybczynski, 2001),
and may also have access to cheaper capital. Enclaves and
TNDs are also likely to have lower per-unit legal and other
costs of buying property compared to infill development.
In other ways, enclaves and TNDs may be more expensive
to build. They may require more land per unit than infill
development, for example, for roadways, landscaping, and
parking areas.
Amenities and Disamenities
Our three urban design types also produce different
types of amenities. Enclaves and TNDs often provide
additional site design amenities such as new infrastructure,
landscaping, and convenient off-street parking. Parking for
infill development by contrast, may be less safe because of
heavier traffic and crime on alleys and streets.' The different
design types also face differing constraints that influence
their locational amenities. For example, enclaves and TNDs
require large parcels, limiting their feasible locations more
than is the case for infill. Finally, differences among design
types may influence social interactions, which in turn in-
fluence residents' safety and participation in neighborhood
civic life (Newman, 1972; Bothwell et al., 1998; Duany et
al., 2000; Jacobs, 1961; Whyte, 1988). Scholars have
argued that existing urban neighborhoods and TNDs
encourage more social interaction, and some have even
tried to quantify these attributes. For example, Eppli and

Table 1. General characteristics of different urban design types.
Number of Number of
developers urban design Physical
per unit decisions integration
Development type Parcel size of land area possible with context
Infill Small Many Few High
Traditional neighborhood development (TND) Large Few Many
Moderate
Enclave Large Few Many Low
101
102 Journal of the American Planning Association, Winter
2007, Vol. 73, No. 1
Figure 1. Typical infill-type housing.
Tu (1999) found that new-urbanist-style developments
commanded higher prices than similar, suburban-style
units. Song and Knaap (2003) found that residents of
new urbanist communities were willing to pay more for
development designed for internal connectivity, but not for
development designed to be integrated with the surrounding
environment.
New housing that is poorly integrated with its sur-
roundings may stigmatize residents, particularly if they are
low-income. Recent redevelopments of public housing
projects have replaced enclave design features with TND
features to integrate this housing better into its surround-
ings (HUD & Congress for the New Urbanism, 2000).

Infill housing is not spatially isolated, and does not distin-
guish itself from its context as enclaves and TNDs do. This
gives residents of new infill less ability to control access by
outsiders, and thus risk, than residents of enclaves and
TNDs have, but this problem may be offset by improved
community social controls, particularly if residents know
each other well (Bothwell et al., 1998; Song & Knaap,
2003).
Future Uncertainty
It is a disadvantage when potential homebuyers "do
not know with certainty how the neighborhood develop-
ment will evolve or proceed over time" (Sirmans, Turnbull,
& Dombrow, 1997, p. 615; see also Thorsnes, 2000).
Different urban design types are associated with different
levels of future uncertainty. The homogeneous nature of
an enclave or TND assures purchasers that future units will
be similar to existing ones. 2 This is generally less true of
infill. One can therefore argue that enclave and TND models
help to internalize some of these potential negative externali-
ties, and residents may be willing to pay a premium for this.
Urban Neighborhoods
103
Figure 2. Typical traditional neighborhood development-type
housing.
Because the influences described above work against
one another, and their magnitudes are unknown, the

literature does not permit strong apriori hypotheses about
which urban design type (infill, TND, or enclave) will be
the most desired and therefore most highly valued. The
following sections describe our empirical investigation to
reveal preferences for the urban design of new housing in
low-income neighborhoods.
Data Collection and Analysis
We assembled construction permit data on all parcels
(land lots and their built improvements, if any) on which
housing units were constructed between January 1, 1993
and December 31, 2001 in census tracts where at least
20% of households had incomes below the federal poverty
line in 1990. This number is widely accepted as a thresh-
old for neighborhood distress (Galster, 2002; Jargowsky
1997). Forty-six percent of Chicago census tracts were
distressed in 1990 using this measure. We excluded from
our analysis census tracts within 2 miles of the Central
Business District (CBD). Although there were distressed
neighborhoods inside this perimeter in 1990, infrastruc-
ture improvements and massive amounts of new public
investment since that time made them highly unusual.
We associated Cook County Assessor's Office data on
new condominiums, attached and detached single-family
homes, and apartment buildings with six or fewer units,
3
with construction permit addresses. Eighty-six percent of
the building permit addresses could be matched to Parcel
Identification Numbers (PINs)4 , yielding 1,227 parcels for
which we had both construction permit addresses and
assessment data. Within this group of records, we had

Urban Neighborhoods 103
2007, Vol. 73, No. 1
Figure 3. Typical enclave-type housing.
complete information for a subset of 823 parcels, including
critical data on the building characteristics of the structures
on each parcel. We report results for both larger (N = 1,227)
and smaller (n = 823) samples for variables for which we
had data in each case.
Dependent Variable
We sought to explain housing value, which we measured
using 2003 assessed values. Because we had parcel-level
data, our dependent variable was the assessed value of an
entire parcel, not of an individual dwelling unit.5 In Cook
County, parcels are supposed to be assessed at 16% of their
estimated market values. We relied on assessments instead
of housing unit sales prices for several reasons. First, in
low-income neighborhoods, where home ownership is less
common, only 17% of our small sample could be matched
to sales transaction data.6 Second, examining only sold
properties may introduce selection bias if this sample is
significantly different from the unsold ones (Gatzlaff &

Haurin, 1997). Third, assessments are good proxies for
market values, as each Chicago parcel is reassessed every
three years based on any recent sales of the parcel in ques-
tion and on sales of comparable parcels. 7 Although the use
of assessed values may introduce some degree of error into
the model, we felt it was likely to be randomly distributed.
The Independent Variable of Greatest
Interest: Urban Design Type
We hypothesized that urban design would have an
effect on assessed housing values even after other attributes
that might influence demand were controlled. Determining
the urban design type of an individual parcel required us to
define the "cluster" of similar new units to which it be-
longed. Since TND and enclave developments contain
Urban Neighborhoods
multiple units by definition, we needed groups of infill
units which would be comparable to these.8
We first address-matched all qualifying building permits
to a GIS database, defined a 250-foot buffer around the
address on each permit, and joined overlapping buffers to
create clusters. We felt that units separated by more than
250 feet would not be visible or closely accessible to each
other, reducing the appropriateness of defining them as a
group. We then eliminated clusters with fewer than 20

total units, since smaller developments might not have the
urban design features needed to identify TND- and enclave-
type developments.
We visited and photographed each cluster and matched
these field data to high-resolution aerial photographs and
GIS figure-ground illustrations to confirm infill clusters
(Google, 2005; City of Chicago Department of Planning
and Development, 2005; Cook County Office of the
Assessor, 2005), then classified each remaining (non-infill)
PIN in our sample as either an enclave or TND by deter-
mining whether the following were present or absent:
1. parking (either a lot or individual spaces) in front;
2. roadways interior to the lot, such as driveways or
access roads;
3. front doors opening onto interior walkways,
roadways, or private open space;
4. extensive buffering (substantial trees, plantings,
open space, or landscaped berms) between the
building and the street; and
5. faýade materials which differed from those of
adjoining buildings.
Developments possessing three or more of the foregoing
attributes we considered to be enclaves, and those lacking
three or more we considered to be TNDs. We used these
criteria as binary variables in later model specifications.
Using multiple design criteria also permitted us to catego-
rize developments that possessed only some of the design
features and to analyze developments with mixed design
features.

Figure 4 shows the geographic distribution of our
sample, and Table 2 shows how the sample broke down by
design type. The distribution was similar in both samples.
In both samples average assessed values were significantly
higher for infill clusters than for either enclave or TND
clusters.
Other Independent Variables
In addition to dummy variables for urban design types
and parcel attributes, we also included other site-specific
variables likely to influence parcel value. Descriptive statis-
105
tics for these and the urban design variables are shown for
both the small and large samples in Table 3.9
Model
We employed a standard hedonic model to regress
urban design features on the assessed values of parcels with
new construction in high-poverty Chicago census tracts.
We adopted the following semi-log functional form because
of an observed nonlinear relationship between assessed
value and key parcel attributes like lot size (see Colwell and
Munneke, 1997):
Ln(Assessed value) =
ot + PX + 8Z +,ySCALE + XENCLAVE + ±TND +E
In the equation above, the dependent variable is the
natural log of a parcel's assessed value in 2003, ot is the
intercept, X represents a vector of characteristics of the
structure on the parcel, Z a vector of neighborhood attri-

butes, SCALE represents number of units in the cluster,
and E represents an error term. The binary variables of
ENCLAVE and TND each take on values of 1 if the parcel
is located in an enclave or TND cluster and 0 otherwise.
These two dummy variables are mutually exclusive.
Results
Table 4 shows our regression results. The adjusted R2
values range from 28% to 86%, indicating that the ex-
planatory power of the models is, in some cases, very high.
In most cases, the coefficients on the independent variables
are as expected. Homes wiath the following attributes were
assessed at higher values: more bedrooms and bathrooms;
recent sales; and locations in higher-income areas, near the
CBD, near Lake Michigan, and near transit stops. However,
we are primarily interested in the urban design variables.
Using both the small and large samples, the coefficients on
the dummy variables for location in an enclave or TND
are negative in Models I and 3. These results suggest
locating in these types of development reduces value: an
identical building constructed as infill is worth much
more. Specifically, location in an enclave decreases housing
value by between 22% (large sample) and 24% (small
sample), and location in a TND development decreases
value by between 21% (small sample) and 27% (large
sample) compared to the same unit built as infill."
We then sought to discover which of the individual
design elements characteristic of TNDs and enclaves
2007, Vol. 73, No. 1

Legend
* CBD & Two Mile Radius
[• Chicago Community Areas
Housing Clusters
A Enclave
Fi Infill
O Neotraditional
Figure 4. Location and urban design type of sample construction
permits.
Urban Neighborhoods 107
Table 2. Percent of sample devoted to each of three urban
design types and mean assessed values.
Percent of samples Mean assessed value
(SD)
Small sample Large sample
Development type (n = 823) (N= 1,227) Small sample Large
sample
Enclave 41% 36% $32,909 $31,872
(16,224) (14,723)
TND 21% 20% $31,372 $30,159

(18,466) (16,467)
Infill 38% 44% $49,688 $41,573
(18,220) (18,083)
housing consumers apparently do not like. In Models 2
and 4 we compared only TND and enclave parcels (i.e. we
excluded infill). We found the majority of the coefficients
on individual design element dummy variables to be
significant under these conditions, and we were able to
increase the explanatory power of the original models by
substituting these criteria for the more general variables
representing urban design types.
Specifically, our results showed residents to prefer
some buffer between their living quarters and the street.
They also preferred to have parking adjacent to the street,
in front of their homes. These urban design features are
characteristic of enclaves, and serve to separate housing
from its surroundings. Two other variables, building mate-
rial different from adjoining, and opens to the yard, had
negative and significant coefficients, suggesting that resi-
dents prefer to be more integrated into their surroundings.
Street-facing building entrances and contextual facades,
both typical of TNDs, increase the value of properties in
contiguous developments. The fifth individual variable, the
presence or absence of a private road, was contradictory:
for the smaller sample it was an asset, while for the larger
sample it was a liability.
Conclusions
Our findings indicate that urban design plays a mean-
ingful role in determining housing values in low-income

Chicago neighborhoods. Most importantly, infill housing
appears to command a value premium, compared to both
TND and enclaves. From this we understand consumers to
value housing that is integrated into its urban context over
housing which is dissociated from it. People may associate
urban developments that are homogeneous and dissociated
from their surroundings with public housing, particularly
in low-income neighborhoods.
We also conclude that the value penalty associated
with TND and enclave developments could be reduced by
better connecting these developments to the existing urban
fabric. Two individual characteristics (front parking and
street buffering) had positive impacts, while two others
(private roadways and non street-facing entrances) had
negative impacts. We conclude that residents valued indi-
vidual urban design elements of both the enclave and TND
models. They seemed to appreciate the convenience and
safety of accessible, visible, parking in front of units; the
privacy provided by separation from the street; and being
a part of their surroundings, as expressed by contextual
facades, street-facing entrances, and a shared public street.
Our findings are consistent with those of previous re-
searchers who found similar preferences among suburban
dwellers (Morrow-Jones et al., 2004; Song & Knaap, 2003;
Talen, 2001).
One caveat is that some of the observed value differ-
ential may be due to different land acquisition and devel-
opment costs for the different design types. However,
interviews with housing developers active in these neigh-
borhoods suggest that lack of economies of scale may add
to the cost of infill development, and that enclaves require
more roadways and landscaping than infill.11 Moreover,
the design criteria variables remained statistically significant

in the models run on data that did not include parcels
developed as infill (Models 2 and 4).
We found that whether contiguous developments are
designed as enclaves or TNDs they are less valuable than
2007, Vol. 73, No. 1
Table 3. Attributes of sampled parcels with new construction
and the distressed Chicago census tracts where they are located,
2003.
Small sample (n = 823) Large sample (N = 1,227)
Mill Max Mean SD Min Max Mean SD
2,946 126,564 38,943 19,390 2,057 126,564 35,750 17,372
10 2.27 1.26
2 18 3.5 1.9
0Masonry exterior construction?
(0 = No, 1 = Yes)
1 .81 .39
520 34,000Square feet of land
Units in parcel
Units in cluster

6
1,957 1,680
1 .82
6 240 82 83
7 1.27 .87
6 240 83 85
Age of unit (years)
Tract median household income ($)
Tract percent owner-occupied
Tract percent Black
Tract percent Hispanic
Any recent sale? (0 = No, 1 = Yes)
Distance to CBD (miles)
Distance to Lake Michigan (miles)
Distance to elevated rail stop (miles)
Percent change in quartersection's
equalized assessed value, 1989-1997
Percent of quartersection's equalized

assessed value in commercial and
industrial uses
In an enclave? (0 = No, I = Yes)
In a TND? (0 = No, 1 = Yes)
Infill? (0 = No, I = Yes)
10 6.07 2.07
12,599 95,075 46,511 21,140
4% 71%
1% 98%
0% 80%
0
2.01 7.67
.22 5.06
.06 1.31
46%
39%
38%
22%

14%
42%
21%
.17 .38
4.02
2.29
1.30
1.30
.55 .30
588% 287% 149%
5% 70%
0
0
0
37% 16%
.41 .49
.21 .41
.38 .49

10 5.47
12,599 95,075 49,182 22,041
4% 71%
1% 98%
0% 80%
0
2.00
39%
31%
23%
16%
39%
20%
1 .12 .32
9.69
.06 5.06
.06 1.31
21%

3.91
2.27
1.34
1.16
.52 .28
588% 317% 151%
5% 70%
0
0
0
36% 15%
.36 .48
.20 .40
.44 .50
infill housing. This confirms the work of those theorists,
beginning with Jane Jacobs, who have argued that urban
development that is integrated is more desirable than that
which is isolated. Our results, showing that both the

enclave- and TND-style design models carry similar value
penalties, challenge the neotraditionalist argument that
TNDs are superior to other models of urban design (see
Duany et al., 2000).
Our results should reassure those who believe that the
best way to revitalize urban neighborhoods is to respect
and augment the urban design character of existing places
rather than to transform them in more dramatic ways.
Cities may want to consider these findings as they establish
both redevelopment guidelines and formal and informal
design standards for publicly assisted housing in distressed
neighborhoods.
Assessed value
Full baths
Bedrooms
2
1
1 1
1

1
1
1
11
Percent of quartersection's equalized
assessed value in commercial and
industrial uses
In an enclave? (0 = No, I = Yes)
-0.543**
(-6.164)
-0.219**
(-4.765)
-5.024**
(-12.192)
Urban Neighborhoods
Table 4. Results of regression model predicting 2003 assessed
value for sampled parcels.
Small sample
Model 1: Model 2: Design

Design type characteristics De
(I) (I)
Full baths 0.029 0.046*
(1.928) (2.234)
Bedrooms 0.078** 0.089**
(6.125) (5.994)
Masonry exterior construction 0.029 0.025
(0 = No, I = Yes) (1.003) (0.886)
Square feet of land -. 000 0.000
(-0.073) (1.705)
Units in parcel -0.112"* -0.1 16**
(-3.789) (-2.974)
Units in cluster -0.000 0.010**
(-1.511) (9.213)
Age of unit (years) -0.029** 0.004
(-4.973) (0.687)
Tract median household income 0.000"* 0.000"*
(11.673) (5.410)
Tract percent owner-occupied -1.679** -3.402**

(-9.069) (-5.848)
Tract percent Black -0.389** 8.894**
(-6.010) (10.834)
Tract percent Hispanic -0.365** 20.919**
(-3.341) (11.543)
Any recent sale? (0 = No, 1 = Yes) 0.077** 0.038
(2.696) (1.417)
Distance to CBD (miles) -0.110"* -0.563**
(-7.774) (-6.492)
Distance to Lake Michigan (miles) -0.406** -0.491"*
(-1.574) (-3.057)
Distance to closest elevated rail stop -0.020 -0.173**
(miles) (-7.495) (-3.446)
Percent change in quartersection's
equalized assessed value, 1989-1997 -0.001"* 0.019"*
(-7.041) (11.381)
Large sample
Model 4: Design
•e characteristics

(I)
-0.000**
-5.215)
0.048**
(3.195)
0.001
(1.479)
0.0 19"*
(2.717)
0.000"*
(6.934)
-0.847**
-4.480)
-0.201 *
-2.418)
-0.005
-0.038)
-0.040**

-3.176)
-0.312**
-4.694)
-0.060**
-3.733)
-0.000
-1.370)
-0.139
-1.304)
-0.203**
-3.496)
-0.000"*
(-9.781)
-0.019
(-1.135)
0.000
(0.723)
-0.013

(-1.419)
0.000"*
(8.946)
-1.710**
(-3.772)
1.945**
(4.703)
3.320**
(4.130)
-0. 104**
(-5.604)
-0.099
(-0.563)
-0.370**
(-8.306)
0.000**
(3.216)
0.496*

(2.536)
109
lodel 3:
sign tyt
(1)
_
(-
2007, Vol. 73, No. 1
Table 4 (continued).
Small sample Large sample
Model 2: Design
characteristics
(t)
Model 3:
Design type
(W)
Model 4: Design
characteristics
(1)

In a TND? (0 = No, 1 = Yes)
Building material different from
adjoining? (0 = No, 1 = Yes)
Served by private road? (0 = No, 1 = Yes)
Parking lot in front? (0 = No, 1 = Yes)
Opens to yard? (0 = No, 1 = Yes)
Buffered from the street?
(0 = No, 1 = Yes)
Constant
Adjusted R
2
N
*p < 0 .0 5 **p <0.01
Acknowledgements
This research was funded by a Lincoln Institute of Land Policy
Planning
and Development Fellowship. We greatly appreciate the
research
assistance we received from Dan Weiske and Nina Savar and
feedback

from participants in the Lincoln Institute of Land Policy
Planning and
Development seminar.
Notes
1. On-street parking may reduce the appeal of nearby
developments by
congesting streets and reducing the "aesthetic appeal of the
neighbor-
hood" (Guttery, 2002, p. 266; see also Bohl, 2000). Other
scholars
disagree, citing narrow streets with on-street parking and slower
traffic
as a positive contributor to perceptions of resident comfort and
safety
(Appleyard, Lynch, & Mier, 1966).
2. Prior research has found that contiguous developments lend
them-
selves to institutional arrangements, such as restrictive
covenants, that
reduce future risks of negative neighborhood effects (see
Alexandrakis &
Berry, 1994; Hughes & Turnbull, 1996; Speyrer 1989). Peiser
(1984)
found slightly higher net benefits to "planned" (i.e., large-scale)

versus
unplanned developments and Ellen, Schill, Susin, and Schwartz
(2001)
found that larger-scale and denser developments had
significantly larger
effects on values in the surrounding areas.
3. We largely avoided issues raised by subsidized housing
developments
by excluding from our sample Class 4 parcels, which are those
developed
by nonprofits.
4. We assumed that construction permits whose addresses we
could not
match to assessment data either had incorrect address
information, were
not built in time to be assessed in 2003, or had been built on
newly
subdivided parcels. In order to determine if we were introducing
bias
into the sample by requiring a match, we regressed critical
locational
data (e.g., distance to CBD) against a binary variable indicating
whether

the construction data were matched or unmatched. In none of
these
regressions was this variable ever statistically significant, and
so we
concluded that successful matches were spatially random.
5. We do, however, account for the number of units in each
parcel by
including this information as an independent variable.
6. We expect that the assessor has access to more complete
transactions
data and that, in reality, a larger share of our sample did indeed
sell.
7. When a new building is built, the assessor reviews
construction cost
information from the building permit and acquires any sales
data. The
most recent sale price is a baseline market value that will be
checked
against adjustment factors generated by regressions of area sale
prices.
8. We developed the following technique to avoid over- and
under-
sampling from clusters. We divided the "total construction
value" listed

on the permit for the proposed project by assumed construction
costs of
Model 1:
Design type
(t)
-0.195**
(-4.291)
-0.243**
(-5.273)
-1.840**
(-11.084)
1.545**
(8.199)
3.853**
(13.031)
-5.227**
(-12.238)
1.513"*
(7.132)
-2.638*

(-2.398)
.862
11.635"*
(78.419)
.679
-0.412"*
(-3.283)
-0.591*
(-3.343)
1.130**
(5.372)
-0.627**
(-2.833)
0.914"*
(5.102)
8.873**
(23.529)
.496
692823
10.661*

(71.190)
.282
511 1227
Urban Neighborhoods
$75,000 per unit, to obtain the number of units. If we could
match
fewer than 20% of these expected units with their PINs from the
Assessor's Office, we eliminated the cluster to avoid under-
sampling it.
In clusters where we matched over 75% of the expected units,
we
randomly eliminated PINs to reduce the match rate to no more
than
75% to avoid over-sampling.
9. Ideally, we would have also controlled for the housing tenure
of each
parcel as well as its land and development costs. Unfortunately,
such
data are considered proprietary information and is generally
unavailable.
10. In a semi-log regression, the coefficient on a dummy
variable can be
interpreted as an elasticity as follows: when TND and
ENCLAVE
change from 0 to 1, the value of the parcel will change by
[Exp(b) -1] x
100%.

11. We conducted interviews with Thrush Development
Corporation
and Applied Real Estate Analysis (AREA).
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III
COPYRIGHT INFORMATION
TITLE: Valuing New Development in Distressed Urban
Neighborhoods
SOURCE: J Am Plann Assoc 73 no1 Wint 2007
WN: 0734902098011
The magazine publisher is the copyright holder of this article
and it
is reproduced with permission. Further reproduction of this
article in
violation of the copyright is prohibited. To contact the
publisher:
http://www.japa.pdx.edu/
Copyright 1982-2007 The H.W. Wilson Company. All rights
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