Smart growth characteristics and the spatial pattern of multifamily housing in US metropolitan areas

Abstract

Chinese

This paper examines the relationship between smart growth policies and other social and physical characteristics and the supply of multifamily housing units in 202 core-based metropolitan statistical areas (CBSAs) in the USA. Data for this study were gathered from the GeoLytics Neighborhood Change Database for the 1980, 1990, 2000 and 2010 US Census. The smart growth characteristics of each CBSA are determined by a smart growth index and a catalogue of urban containment rankings, while other social and physical characteristics are obtained from the US Census. This allows us to examine potential differences in development patterns between CBSAs with varying levels of sprawl and growth containment policies. Two regression models are used to determine statistically significant relationships between multifamily development patterns and growth management policies, as well as other social and physical characteristics. The results indicate that high levels of sprawl are associated with relatively fewer multifamily units, especially in suburban areas. In addition, several population demographics that may potentially benefit from multifamily units, such as senior citizens, the population in poverty and smaller households, are less likely to live in areas with higher rates of multifamily units. These findings indicate that planners and policymakers should consider the needs of more diverse communities when planning for housing, especially in suburban areas, where housing diversity is constricted.

Keywords

housing multifamily housing smart growth sprawl urban containment

Introduction

Multifamily units are an important element of smart growth policies, as they have the potential to meet several important goals. Multifamily units can help increase residential density, contribute to a more diverse land use mix, add to a greater range of housing choices, preserve open space and create the conditions for more diverse transportation choices. New multifamily construction can also help revitalise neighbourhoods and increase affordable housing options (Biddle et al., 2006; Colton and Collignon, 2001; Hess, 2005; National Association of Home Builders, 2002). Recent studies have noted that while multifamily units still only account for a small share of overall units in suburban areas, that share has steadily increased over the past four decades (Larco, 2010; Moudon and Hess, 2000). Reasons for this increase include a suburban population that is growing more diverse and changing residential preferences. But multifamily development outside of central cities is often difficult, as multifamily units are associated with many of the perceived negative aspects of urban life. Smart growth policies such as urban containment seek to increase the amount of dense and affordable housing such as multifamily units throughout all areas of the metropolitan region.

But little is known about the impact of broad-based smart growth policies such as urban containment on multifamily development throughout the entire metropolitan region. Understanding the relationship between multifamily development patterns and urban containment will be important to planners and policymakers as more people seek alternatives to single-family houses and as metropolitan regions respond by considering policies that diversify housing stock (Myers and Gearin, 2001; Nelson, 2006b). In addition, understanding the social characteristics of cities and regions with greater concentrations of multifamily units can help planners and policymakers understand the context where multifamily units are developed and better plan for a housing stock that adequately meets demand.

This study examines rates of multifamily housing development within metropolitan areas in the USA from 1980 to 2010. The primary research question we ask is whether multifamily development at a regional scale is related to levels of sprawl and the presence of urban containment policies. We also examine how rates of multifamily development differ between central cities and their suburban areas. In addition, we ask whether multifamily development concentrations are related to social demographics. This may offer new insights as to the usefulness of building multifamily units in areas that have traditionally fought them. By drawing a distinction between central city and suburban locations, this study highlights the role of multifamily development as an important component of growth management and sprawl reduction. Furthermore, by examining central city and suburban areas separately, this study highlights how multifamily units develop within different spatial contexts. We find that urban containment appears to have a more significant influence on multifamily development at the regional level, as opposed to the city or suburban level. In addition, the findings also reveal a potential mismatch between the multifamily unit supply and demographic groups that could benefit from them, such as senior citizens and those living in poverty.

Literature review

Sprawl has been the primary pattern of urban development in the USA during the post-Second World War era, leading to the deconcentration of population and employment in metropolitan areas (Burchfield et al., 2006; Downs, 1998; Fulton et al., 2001). Smart growth policies emerged as public officials and citizens grew concerned with the negative effects of sprawling, low-density, auto-oriented urban development. These include negative environmental effects, the loss of farmland and open space, increased infrastructure costs and increased congestion (Chapin, 2012; Ewing, 1997). Smart growth seeks to reduce sprawl by redirecting urban development away from the urban fringe and into existing urban areas. In addition, smart growth objectives include producing more compact development patterns, conserving land, increasing transportation options and transportation equity, reducing greenhouse gas emissions, producing more affordable housing and ensuring greater fiscal savings (Barbour and Deakin, 2012; Ingram and Hong, 2009; Pendall and Nelson, 2005).

Multifamily units are an important component of smart growth policies, but multifamily development patterns remain an understudied area within planning literature. Little is known about the social or physical characteristics of neighbourhoods that receive multifamily development. Likewise, the relationship between multifamily development and urban sprawl remains underdeveloped. This is due to a lack of data on multifamily units (Bogdon and Follain, 1996; Peiser 1989), and because multifamily units are typically not viewed as an important aspect of urban development within a regional context, since sprawl is primarily driven by single-family subdivision development (Hess, 2005; Wheeler, 2008). Nelson et al. (2004a) found that regions with smart growth policies have greater rates of multifamily development within central cities than those without, while Ingram and Hong (2009) found that states with smart growth policies had higher rates of multifamily construction, but these higher rates did not result in greater housing affordability, as states with smart growth policies had a greater share of cost-burdened homeowners. Additionally, Nelson et al. (2004b) found that central cities in states or regions with urban containment policies adopted prior to 1985 experienced greater levels of multifamily construction. In contrast, Levine (1999) found that local smart growth policies in California municipalities displaced multifamily housing to neighbouring communities, which contributed to sprawl.

Several recent studies have conducted longitudinal analyses of metropolitan statistical areas (MSAs) in the USA in order to measure the impact of growth management policies. These include studies of growth management’s effect on sprawl (Carruthers, 2002; Fulton et al., 2001; Yin and Sun, 2007), central city revitalisation (Dawkins and Nelson, 2003), racial segregation (Nelson et al., 2004b), sustainable transportation (Deal et al., 2009) and active commuting patterns (Aytur et al., 2008). Other studies have examined the impact of growth management on sprawl by conducting cross-sectional analyses of a large number of MSAs (Ewing et al., 2002; Wassmer, 2006; Woo and Guldmann, 2014). Pendall and Nelson (2005) found that regulations on growth will not necessarily influence the rate of housing development, housing prices or the overall makeup of the housing stock. This may be because such regulations are more likely to be enacted in high-priced locales.

Demand for multifamily units has increased in the wake of the recent foreclosure crisis and subsequent recession, and multifamily’s share of new residential construction is expected to increase even as the housing market recovers (Freddie Mac, 2012). Many attribute this to demographic changes such as an aging population and shrinking household sizes (Colton and Collignon, 2001; Danielsen and Lang, 1998; Myers and Pitkin, 2009; Nelson, 2006a), and many have predicted that baby boomers will seek multifamily units as they become empty nest households. As the largest age cohort, the baby-boom generation created a surge in housing demand at each life stage (Myers and Ryu, 2008). As a result, postwar suburbia was characterised by single-family homes, adequate for the nuclear family and child rearing (Filion et al., 1999). Empty nest households have no need for things such as quality schools and other amenities associated with single-family suburbs. The model predicts that as baby-boomer-headed households become empty nest or single-person households, they will seek to downsize their housing consumption. But boomers do not appear to be relocating to urban areas in large numbers or reducing their housing consumption (Fannie Mae, 2014; Venti and Wise, 2001). Of all baby boomers, 70–80% would prefer to stay in their homes as they age (Kochera et al., 2005; Koppen, 2009).

The general lack of availability of multifamily units may be linked to current preferences and demand. Multifamily development in many areas of the metropolitan region is difficult, as multifamily housing in the USA has historically been associated with negative aspects of central city neighbourhoods. Many municipalities in the USA excessively restrict multifamily development through exclusionary zoning and restrictive building codes. Some have argued that local restrictions on multifamily construction and policies favourable to single-family homeownership result in an undersupply of multifamily units (Glaeser, 2011; Levine, 2006; Schuetz, 2008). Similarly, others note that in order to ensure that multifamily supply adequately meets demand, multifamily units should be specifically addressed as a part of regional and/or smart growth policies (Chakraborty et al., 2009; Danielsen et al., 1999). For example, Portland, Oregon sets construction goals for multifamily units within its urban growth boundary (Katz et al., 2003). Chakraborty et al. (2009) argued that regional governments should create minimum density requirements and develop incentives for multifamily development.

Some recent studies have examined the presence and form of multifamily units that already exist in the suburbs. Farris (2001) examined 22 older metropolitan areas in the USA and found that from 1989 to 1998, central cities only accounted for 15% of all multifamily construction permits. While multifamily units are still primarily associated with the inner city, the share of multifamily units in the suburbs has steadily grown since the 1970s. In 2005, multifamily units accounted for 14% of all suburban housing units (Hess, 2005; Larco, 2010). Hess (2005) and Larco (2009) note that multifamily units can be found dispersed throughout the suburbs and that these units could be retrofitted to meet smart growth goals. Larco argues that in order to meet smart growth goals, planners and policymakers should allow for alternatives to the enclaved, auto-oriented patterns that typify suburban multifamily development. Clusters of multifamily housing could be viewed as semi-urban nodes, with small blocks able to accommodate dense retail and connections for pedestrians and cycling activity. In many municipalities, this would require the elimination of land use policies that mandate segregated land uses as well as new zoning policies crafted specifically for mixed use and multifamily developments.

Furthermore, Gober and and Burns (2002) and Atkinson-Palombo (2010) found that multifamily development in the Phoenix region in the 1990s and early 2000s was not concentrated in the central city, but ‘filled in’ parcels that were left open after an initial wave of single-family development. Additionally, multifamily construction at the urban fringe doubled in the early 2000s. Wheeler’s (2008) examination of urban development patterns from 1980 to 2005 found that many new single-family subdivisions appeared to deliberately reserve land for future multifamily use. These findings are in accord with Ohls and Pines’ (1975) model for discontinuous urban development, which posited that parcels left open after initial development will increase in value, making high-density construction, such as multifamily units, the most economically attractive development choice.

Moudon and Hess (2000) found that suburban multifamily developments tend to cluster. These appear in both low- and high-income suburban neighbourhoods, as well as in older and newer suburban neighbourhoods. Multifamily clusters are generally unplanned, and differ from edge cities (Garreau, 1991) in that they are not commercial centres, but primarily residential. Moudon and Hess’ study of these clusters in Seattle’s suburbs found that these multifamily clusters housed twice as many racial and ethnic minorities.

Multifamily units are key components of neotraditional planning paradigms such as transit-oriented development and New Urbanism (Calthorpe, 1993; Duany et al., 2001; Haughey, 2005). But these communities account for only a small part of new multifamily development. Generally, multifamily structures outside of the central city follow the same segregated land use pattern of suburban single-family subdivisions. Multifamily units tend to be located in less desirable locations, such as next to freeways, arterials, railroads, and large retail and industrial areas. These locations are meant to ensure that multifamily developments do not intrude on single-family subdivisions. Additionally, multifamily developments often act as a buffer between single-family subdivisions and retail areas (Atkinson-Palombo, 2010; Baar, 1992; Larco, 2009; Moudon and Hess, 2000; Wheeler, 2008). Often, the streets surrounding these multifamily developments are not outfitted with sidewalks, nor are there pedestrian connections between these multifamily developments and adjacent retail (Hess, 2005; Larco, 2009). The precedence for treating multifamily units as either buffers or as secondary units dates to the influential neighbourhood designs of Clarence Perry and Clarence Stein. By placing these units at the edge of neighbourhoods, these early 20th century plans helped foster the popular notion that multifamily housing should occupy areas that were inappropriate for single-family or commercial use (Gravin, 2002).

In summary, multifamily units have long been a part of the suburban landscape in the USA, despite a range of policies and planning initiatives that preference single-family development over all potential alternative residential types. Additionally, recent studies have posited that changing demographics and housing preferences are likely to increase demand for multifamily units in both urban and suburban areas. Smart growth policies should increase multifamily development, either through explicit policies that address multifamily development, or through market forces that respond to limited developable areas by building new residential units at a higher density. While recent studies have noted the existence and form of multifamily units in the suburbs, there is still much that is not known about multifamily spatial development throughout entire regions, or the relationship between suburban multifamily development, sprawl and smart growth. This paper addresses these concerns by employing a large-scale examination of multifamily development patterns for the largest metropolitan regions in the USA.

Methodology

This study examines the relationships between multifamily development rates and variables related to growth management, sprawl and important social characteristics within metropolitan regions. To accomplish this, the study constructs and separately examines three geographical units of analysis: (1) selected Metropolitan Statistical Areas (MSAs), (2) the central cities of the selected MSAs and (3) the suburban areas of the selected MSAs. MSAs are US Census-designated continuous geographic regions that contain an urban core area with a population of at least 50,000, and all surrounding counties that are socioeconomically linked to the core. The central cities of the MSAs were defined in terms of the 2010 US Census as the municipality in an MSA with the largest population. In certain cases, additional municipalities within a MSA may also qualify as central cities if they have certain population or commuting characteristics. The suburban areas of the MSAs were defined as all areas within the MSA not classified as the central city. In addition, this study included regional dummy variables that classified MSAs in the USA as one of nine geographical areas. This allows us to examine potential variation in multifamily development based on location. This is potentially important because urban development patterns can vary by region. For example, suburban tract housing in the West is typically more densely developed than in the Midwest or Northeast (Lopez, 2014; Wheeler, 2008). These regional variations may produce different levels of multifamily construction.

Data from the 1980, 1990, 2000 US Census and the 2006–2010 ACS was collected for 363 MSAs in the continental USA delineated by the 2010 Census. Some of the smaller MSAs lacked population, housing unit, or housing price Census data for all four decades, or were not defined as MSAs in earlier years. These regions were dropped. As a result, this study includes 202 of the 363 MSAs in the USA. This sample skews towards the larger MSAs, and includes 200 of the 205 largest MSAs in the USA based on the Census 2010 population. In 2010, the mean MSA population of this study’s sample was about 1.13 million while the mean population for all MSAs was 915,138. A difference in means test also shows that the MSAs included in this analysis also experienced greater average rates of population and housing unit growth from 1980 to 2010. As such, the results of this study may not apply to smaller MSAs or regions in the USA.

Multifamily units are the key interest in this study, but there is no single definition as to what constitutes a multifamily unit or a multifamily structure. The US Census (2012) defines housing units as multifamily if they are in a structure that contains two or more units, while the Urban Land Institute (2011) defines a structure as multifamily if it contains ten or more units. This study uses the definition of the National Association of Home Builders (2002): multifamily units are residential units in structures that contain five or more units. This definition will exclude duplexes and smaller townhouse structures, although these structures often share characteristics of both single-family and multifamily housing (Larco, 2010). Many previous studies defined multifamily as rental-only housing (Colton and Collignon, 2001; Follain, 1994), but because this study focuses on housing development and not tenure, it will include both owner-occupied and renter-occupied multifamily units. This means that the multifamily units examined in this study are not necessarily affordable housing. Many newer multifamily units being developed in suburban areas are luxury condominiums (Atkinson-Palombo, 2010; Colton and Collignon, 2001). But the presence of multifamily units may highlight areas that have a high potential for affordable housing development (Carruthers, 2003; Pendall, 2006). Furthermore, the presence of multifamily units may also indicate areas where smart growth goals can be implemented with greater ease.

Demographic variables were compiled into a cross-sectional longitudinal data set of housing units by type from the 1980, 1990, 2000 and 2010 US Census and the 2006–2010 American Community Survey. Descriptive statistics are shown in Table 1. The mean MSA population was about 950,000, and the standard deviation indicates that MSA population varied widely, with New York-White Plains-Wayne NY/NJ as the largest MSA and Sioux Falls, SD as the smallest, based on 2010 population counts. Additionally, this study employs a catalogue of urban containment developed by Dawkins and Nelson (2003) and Wassmer (2006), which determined the type and relative strength of urban containment for the largest metropolitan regions in the USA. Urban containment, or growth management, is considered one of the most effective implementations of smart growth across a wide geographic area.

Table 1.

Variables and descriptive analysis by subareas (pooled data 1980–2010).

	Variables	Descriptions	Obs.	Mean	Std. dev.	Min	Max
Dependent variables	MFH_LQ	LQ multifamily hu (CBSA level)	808	0.753	0.293	0.231	1.939
	MFH_LQC	LQ multifamily housing units (central city)	808	0.760	0.275	0.035	2.188
	MFH_LQS	LQ multifamily housing units (suburbs)	808	0.714	0.395	0.069	2.644
Ind. variables (All CBSAs)	Pop.	Pop. Size (10,000)	808	95.096	179.515	8.593	1890.00
	OLD	65+ Elderly pop. (%)	808	11.796	3.010	4.448	25.410
	MIN	Minority population (%)	808	19.086	11.232	0.960	51.706
	POV	Pop. under poverty (%)	808	13.120	4.720	4.453	41.877
	HHSIZE	Household size	808	2.726	0.235	2.151	3.868
	HOWN	Home ownership (%)	808	60.830	5.670	41.960	89.790
	AHP	Ave. housing price (US$1000)	808	134.791	99.553	32.572	727.08
	SI	Sprawl index	808	60.196	15.564	11.385	91.806
Ind. variables (Central Cities)	Pop.	Pop. Size (10,000)	808	38.958	82.674	1.856	856.430
	OLD	65+ Elderly pop. (%)	808	12.279	3.439	3.983	44.670
	MIN	Minority population (%)	808	27.711	15.365	1.211	80.359
	POV	Pop. under poverty (%)	808	17.375	5.480	5.716	40.960
	HHSIZE	Household size	808	2.615	0.255	1.987	3.982
	HOWN	Home ownership (%)	808	50.546	7.412	22.176	85.368
	AHP	Ave. housing price (US$1000)	808	124.380	96.752	22.907	749.407
	SI	Sprawl index	808	60.196	15.564	11.385	91.806
Ind. variables (Suburbs)	Pop.	Pop. size (10,000)	808	56.147	102.473	0.252	1033.280
	OLD	65+ Elderly pop. (%)	808	11.545	3.415	3.540	29.498
	MIN	Minority population (%)	808	13.962	10.277	0.702	45.219
	POV	Pop. under poverty (%)	808	10.817	5.475	2.687	47.526
	HHSIZE	Household size	808	2.813	0.285	1.918	4.582
	HOWN	Home ownership (%)	808	67.972	6.610	45.414	100.000
	AHP	Ave. housing price (US$1000)	808	140.075	105.022	20.569	840.213
	SI	Sprawl index	808	60.196	15.564	11.385	91.806
	Year	1980, 1990, 2000, 2010	808	−	−	1980	2010
	UCONT	Dummy for urban contain.	808	0.351	−	0	1
	dregion1	Dummy for East North Cent.	808	0.158	−	0	1
	dregion2	Dummy for East South Cent.	808	0.074	−	0	1
Ind. variables	dregion3	Dummy for Middle Atlantic	808	0.094	−	0	1
	dregion4	Dummy for Mountain	808	0.069	−	0	1
	dregion5	Dummy for New England	808	0.040	−	0	1
	dregion6	Dummy for Pacific	808	0.158	−	0	1
	dregion7	Dummy for South Atlantic	808	0.213	−	0	1
	dregion8	Dummy for West North Cent.	808	0.059	−	0	1
	dregion9	Dummy for West South Cent.	808	0.134	−	0	1

This index is based on a panel data set from 1980 to 1998 for 293 MSAs in the USA. It identifies 127 regions with urban containment policies. Dawkins and Nelson (2003) assembled a catalogue of urban containment policies through an extensive review of local and regional planning documents and policies. Urban containment can take several forms, and they define an urban containment boundary as ‘the presence of policies that are explicitly designed to limit the development of land outside a defined urban area, while encouraging infill development and redevelopment inside the urban area’ (Nelson, 2004: 1).

A dummy variable was created for all MSAs that has one or more of the urban containment forms as designated by Nelson and Dawkins. In total, 71 of the 202 MSAs in this dataset, or about 35%, included an area that had implemented urban containment of some form. Because urban containment policies will not likely have an immediate impact on development patterns, we accounted for a lag of time by only identifying a region as having urban containment if the policy was implemented more than ten years after the date of assessment.

Additionally, the study employed an urban sprawl index to measure the relative levels of sprawl for the 202 CBSAs examined. Several methodologies have been developed to measure sprawl (Ewing et al., 2002; Fulton et al., 2001; Galster et al., 2001; Lopez and Hynes, 2003). This uses the index developed by Lopez and Hynes (2003), as it accounts for sprawl’s multifaceted nature. This index examines residential density and overall patterns of decentralisation by measuring the mix of high-density Census tracts (more than 3500 persons per square mile), and low-density tracts (200–3500 persons per square mile). The formula for this sprawl index is:

S I_{i} = (\frac{L_{i} - H_{i}}{100 + 1}) \times 50

(1)

where SI_i is the sprawl index for CBSA i, H_i is the percentage of total population in high-density Census tract i, and L is the percentage of total population in low-density Census tract i. The index ranges from 0 (least amount of sprawl) to 100 (greatest amount of sprawl). This index may fail to account for differences in metropolitan size, but it is useful for longitudinal studies such as this one.

This study used a location quotient (LQ) formula to determine the rate of multifamily unit development within MSAs, as well as within the central cities and suburbs of the MSAs. An LQ formula offers a measurement of the relative concentration of an object within a region compared to a larger geographic area. The multifamily LQ of an MSA is determined by dividing the total number of multifamily units within a single MSA by the total number of multifamily units in all MSAs:

M F H_L Q_{i} = \frac{(M S A_{i} m u l t i f a m i l y h o u s i n g u n i t / C B S A_{i} t o t a l h o u s i n g u n i t)}{(M S A_{a l l} m u l t i f a m i l y h o u s i n g u n i t / C B S A_{a l l} t o t a l h o u s i n g u n i t)}

(2)

where MFH_LQ_i is the LQ of multifamily units in MSA_i. Likewise, the formulas for determining the LQ of multifamily units within central cities and suburban municipalities are:

MFH_L Q_{icity} = \frac{(MS A_{iccity} multifamily housing unit / CBS A_{iccity} total housing unit)}{(MS A_{all_ccity} multifamily housing unit / CBS A_{all_ccity} total housing unit)}

(3)

MFH_L Q_{isub} = \frac{(MS A_{isub} multifamily housing unit / CBS A_{isub} total housing unit)}{(MS A_{all_sub} multifamily housing unit / CBS A_{all_subs} total housing unit)}

(4)

where MFH_LQ_icity is the LQ of multifamily units within the central city of a MSA, and MFH_LQ_isub is the LQ of multifamily units within all suburban municipalities of a MSA.

LQ values higher than 1 indicate that the concentration of multifamily units within an observed area is greater than the overall concentration of multifamily units within all MSAs or within all central cities or suburban subsets of the MSAs observed. The LQ method for determining the relative concentration of multifamily units is useful because the value range is easier to interpret than comparing the overall percentages of multifamily units. Table 1 shows that the mean LQ for all MSAs is about 0.75, indicating that the majority of MSAs in this study had fewer multifamily units that the average number for all MSAs. Likewise, the mean LQ for central city areas was only slightly higher at about 0.76, while the mean LQ for suburban areas was lower, at about 0.71. The standard deviations indicate that suburban areas had the greatest LQ variation.

This study employed a panel data set that tracked changes in the multifamily LQ, as well as with several explanatory variables during each decennial Census from 1980 to 2010. The repeated observations of the same areas minimises the possibility for unobserved contributing factors impact the measured variables. In addition, sampling error is reduced. Two regression models were employed to examine the relationship between the multifamily LQ and within central cities, suburbs and entire MSAs. Both a Pooled OLS and a Random-Effects GLS regression were used to examine the effect of several demographic, physical and geographic variables on the rate of multifamily development. The Pooled OLS model is similar to the standard OLS model in that one or more independent variables are regressed on a dependent variable, so that:

y_{it} = α_{it} + x_{it}' β_{it} + μ_{it}

(5)

where y_it is the dependent variable, $α_{it}$ captures unobserved heterogeneity, $x_{it}' β_{it}$ represents the vector of independent variables, and $μ_{it}$ represents the error term. Pooled OLS models are not able to control for the correlation of errors for an individual observation over different time periods. As such, the standard errors may be underestimated, and t-statistics may be overestimated. The random effects GLS model assumes that the unobservable individual effect variables for the intercept αi are random. In cases such as this study, where the data contain a large number of observations and a small number of time periods, it is standard to allow different intercepts at each time period. The aggregate effects of time will influence the dependent variable equally. As such, both pooled OLS and a random effects GLS models were employed for this panel data set.

Analysis and results

This study examined the relationship between the multifamily LQ in suburbs, central cities and entire MSAs and their relationship to various physical and social variables. Table 2 shows the overall growth of multifamily housing from 1980 to 2010 within the 202 MSAs examined in this study. Overall, the number of multifamily units increased by about 48%, somewhat less than the 57% increase in all housing units during this time period. Suburban areas added slightly more multifamily units in the time period examined, 3.5 million, than central cities, which added 3 million units. Additionally, multifamily units saw the greatest increase in suburban municipalities, with a growth rate of about 73%. In contrast, multifamily production in central city areas increased by about 34%. Despite this high rate of growth in the suburbs, the overall share of multifamily units in the suburbs fell about 0.5%, while the overall share of multifamily units in central cities increased by about 0.75%.

Table 2.

Total and multifamily unit production in selected CBSAs, 1980–2010.

	Area	1980	1990	2000	2010	1980–2010
Total housing units	central city	28,319	31,493	33,733	36,994	30.6%
	suburbs	31,059	40,781	48,448	56,114	80.7%
	total	59,378	72,274	82,181	93,108	56.8%
Multifamily housing units	central city	8,512	9,690	10,286	11,398	33.9%
	suburbs	4,646	6,001	6,892	8,031	72.8%
	total	13,158	15,692	17,179	19,429	47.7%
% of Multifamily housing units	central city	30.1	30.8	30.5	30.8
	suburbs	15.0	14.7	14.2	14.3
	total	22.2	21.7	20.9	20.9

Note: housing units: 1,000

Source: US Census 1980, 1990, 2000, 2010.

Table 3 shows the regression results for multifamily LQ throughout the entire MSA. Both Pooled OLS and Random-Effect GLS results are shown. Based on the Breusch-Pagan (BP) test for heteroscedasticity, GLS is the better model than the Pooled OLS. As such, the bulk of this analysis will focus on the random-effect GLS results. We find that the urban containment dummy is significant, although the predicted impact of urban containment policies is small, at least at the level of the entire MSA. The sprawl index variable is significant, producing a negative coefficient, indicating that MSAs with less sprawl will produce more multifamily units. Furthermore, the population size has a positive and significant effect on the rate of multifamily unit development. This is to be expected, as MSAs with higher populations are likely to experience higher population densities, requiring more multifamily units. Interestingly, the random effect model also shows a substantial significant negative result for the elderly population and household size. Recent literature has pointed to these demographic groups as among those with the greatest potential to benefit from multifamily housing (Colton and Collignon, 2001; Prosper, 2004). This points to a potential discrepancy between housing supply and housing demand.

Table 3.

Regression analysis for multifamily housing unit in all CBSAs.

LQ for multifamily housing unit in all CBSAs	Pooled OLS			Random-effect GLS
LQ for multifamily housing unit in all CBSAs	Coef.	t	p > z	Coef.	z	p > z
Pop. size	0.0000 ***	10.99	0.000	0.0000 ***	8.72	0.000
Elderly (%)	−0.0104 ***	−3.79	0.000	−0.0172 ***	−6.20	0.000
Minority (%)	−0.0026 ***	−3.22	0.001	0.0008	0.86	0.389
Poverty (%)	−0.0096 ***	−4.69	0.000	−0.0028	−1.70	0.088
HH size	−0.2400 ***	−6.33	0.000	−0.4054 ***	−13.82	0.000
Home ownership	−0.0269 ***	−19.19	0.000	−0.0134 ***	−12.92	0.000
Ave. home value	0.0000 ***	5.72	0.000	0.0000 **	3.72	0.000
Sprawl index	−0.0055 ***	−9.43	0.000	−0.0029 ***	−4.77	0.000
Urban containment	0.0270	1.46	0.145	0.0272 ***	2.91	0.004
Year 1980	0.0994 ***	5.34	0.000	0.0580 ***	6.11	0.000
Year 2000	0.0340	1.86	0.063	0.0038	0.48	0.634
Year 2010	−0.0270	−1.09	0.274	−0.0138	−1.15	0.250
East North Central	0.0166	0.66	0.509	0.0045	0.10	0.920
East South Central	0.0400	1.41	0.159	−0.0371	−0.69	0.493
Middle Atlantic	−0.1252 ***	−4.28	0.000	−0.0863	−1.65	0.099
Mountain	−0.0500	−1.63	0.104	0.0353	0.63	0.531
New England	−0.0899 **	−2.34	0.020	0.0147	0.21	0.831
Pacific	−0.2106 ***	−8.09	0.000	−0.0751	−1.67	0.094
South Atlantic	0.0424	1.74	0.082	0.0053	0.12	0.902
West North Central	0.0594	1.82	0.069	0.0645	1.08	0.279
Constant	3.5364 ***	26.96	0.000	2.9874 ***	22.52	0.000
sigma_u					0.152
sigma_e					0.056
rho					0.880
observation	808				808
number of groups					202
F(20, 787)	85.98***
Wald chi²(20)					524.09***
BP test: chibar²(01)					745.32***
R-squared within					0.237
between					0.636
overall	0.678				0.618

Notes: **p < 0.05, ***p < 0.01.

Table 4 shows the results for multifamily LQ values when only the central cities of MSAs are included in the model. While the population size for the entire MSA model was significant, its predicted effect on multifamily LQ is far greater than the effect for entire MSAs. This indicates that the central city population has a far more elastic effect on multifamily production than the population of an entire region. Similarly, the effect of the share of the elderly population is much more substantial than at the total MSA level. This, along with a similar result in intensity for the population in poverty indicates that there is a potential for a housing mismatch in these areas. Similarly, central cities in MSAs with less sprawl will have a higher multifamily LQ.

Table 4.

Regression analysis for multifamily housing unit in central cities.

LQ for multifamily housing unit in central cities	Pooled OLS			Random-effect GLS
LQ for multifamily housing unit in central cities	Coef.	t	p > t	Coef.	z	p > z
Pop. size	0.0000 ***	9.89	0.000	0.0011***	9.06	0.000
Elderly (%)	−0.0042 **	−2.07	0.039	−0.0063***	−3.54	0.000
Minority (%)	−0.0024 ***	−5.13	0.000	0.0001	0.23	0.821
Poverty (%)	−0.0072 ***	−4.82	0.000	−0.0030**	−2.55	0.011
HH size	−0.1989 ***	−6.71	0.000	−0.2755***	−11.5	0.000
Home ownership	−0.0264 ***	−23.91	0.000	−0.0142***	−17.72	0.000
Ave. home value	0.0000 ***	9.21	0.000	0.0003***	5.61	0.000
Sprawl index	−0.0026 ***	−5.33	0.000	−0.0017***	−3.08	0.002
Urban containment	0.0087	0.53	0.597	0.0483	1.91	0.056
Year 1980	0.0994 ***	6.24	0.000	0.0537***	6.74	0.000
Year 2000	0.0039	0.24	0.809	−0.0003	−0.04	0.970
Year 2010	−0.0957 ***	−4.45	0.000	−0.0389***	−3.35	0.001
East North Central	−0.0460 **	−2.28	0.023	−0.0718	−1.88	0.060
East South Central	0.0333	1.33	0.185	−0.0091	−0.19	0.848
Middle Atlantic	−0.2381 ***	−9.55	0.000	−0.1631***	−3.68	0.000
Mountain	−0.0342	−1.31	0.192	0.0226	0.46	0.645
New England	−0.1002 ***	−3.1	0.002	0.0605***	1.03	0.303
Pacific	−0.1774 ***	−8.11	0.000	−0.0960**	−2.25	0.025
South Atlantic	−0.0047	−0.22	0.824	−0.0037	−0.1	0.924
West North Central	0.0570 **	2.07	0.039	0.0366	0.72	0.474
Constant	2.9273 ***	29.88	0.000	2.3613***	22.79	0.000
sigma_u					0.131
sigma_e					0.052
Rho					0.864
observation	808				808
number of groups					202
F(20, 787)	101.57***
Wald chi²(20)					738.18***
BP test: chibar²(01)					679.54***
R-squared within					0.357
between					0.671
overall	0.714				0.654

Notes: **p < 0.05, ***p < 0.01.

This is not surprising. Although Table 4 also shows that the decrease in the Multifamily LQ leads to a decrease in the share of the elderly population more so than in the entire MSA. But the negative relationship between senior citizens, the population in poverty, and the average household size in central cities indicates that the potential housing mismatch is not just a product of sprawl and restrictive suburban zoning. The results show that MSAs in the Mountain and New England parts of the country have the highest LQ rates. This makes sense in that New England cities are more compact, and are likely to construct more multifamily units as a result.

Table 5 shows the results for multifamily LQ values when only the suburban municipalities of all MSAs are included in the model. The results for this suburban model are closer to the overall MSA results than the central city results. This may be because suburban areas have experienced the greatest amount of urban growth over the course of the last 30 years. The effect for elderly population share, while significant, is greater than in central cities. Unlike in the central cities model, the effect of the population in poverty is significant at the 1% level and it is negative. The sprawl index dummy is also significant and has a greater effect on suburban areas than in the central city model.

Table 5.

Regression analysis for multifamily housing unit in suburbs.

LQ for multifamily housing unit in suburbs	Pooled OLS			Random-effect GLS
LQ for multifamily housing unit in suburbs	Coef.	t	p > t	Coef.	z	p > z
Pop. size	0.0000 ***	8.24	0.000	0.0000 ***	5.87	0.000
Elderly (%)	0.0025	0.59	0.555	−0.0126 ***	−3.38	0.001
Minority (%)	−0.0012	−0.79	0.430	−0.0001	−0.06	0.954
Poverty (%)	−0.0100 ***	−3.34	0.001	−0.0068 ***	−2.88	0.004
HH size	−0.2283 ***	−4.14	0.000	−0.4607 ***	−12.12	0.000
Home ownership	−0.0273 ***	−14.71	0.000	−0.0123 ***	−10.03	0.000
Ave. home value	0.0000 ***	4.03	0.000	0.0000 *	1.83	0.068
Sprawl index	−0.0074 ***	−8.18	0.000	−0.0047 ***	−5.06	0.000
Urban containment	0.0339	1.15	0.252	0.0273	1.84	0.066
Year 1980	0.1899 ***	6.43	0.000	0.1002 ***	7.25	0.000
Year 2000	0.0352	1.20	0.229	0.0104	0.85	0.396
Year 2010	−0.0331	−0.87	0.385	0.0259	1.46	0.144
East North Central	0.2562 ***	6.28	0.000	0.2120 ***	2.94	0.003
East South Central	0.0911 **	2.00	0.046	−0.0101	−0.12	0.907
Middle Atlantic	0.0618	1.30	0.193	0.0807	0.97	0.334
Mountain	0.0025	0.05	0.958	0.0631	0.71	0.476
New England	0.0241	0.39	0.695	0.0695	0.64	0.525
Pacific	−0.1155 ***	−2.72	0.007	0.0583	0.82	0.415
South Atlantic	0.1909 ***	4.80	0.000	0.1599 **	2.35	0.019
West North Central	0.1535 ***	2.94	0.003	0.0841	0.89	0.371
Constant	3.4609 ***	14.51	0.000	3.1379 ***	15.90	0.000
sigma_u					0.247
sigma_e					0.091
rho					0.880
observation	808				808
number of groups					202
F(20, 787)	47.65***
Wald chi²(20)					360.37***
BP test: chibar²(01)					760.87***
R-squared within					0.232
between					0.470
overall	0.5362				0.456

Note: **p < 0.05, ***p < 0.01.

The share of minority population is not significant. This may be due to the vast variety in the suburbs. If anything, this points to the need to greater understand suburban heterogeneity. In contrast, we find that the share of the population in poverty has a negative effect of over twice the magnitude on multifamily LQ than in entire MSAs or in central cities. This indicates that population share in the suburbs is more associated with lower LQ within the entire suburban area. Could the amount of people in poverty influence multifamily construction? Are low poverty people in suburbs concentrated compared with other areas? We see that while urban containment is only significant at the 10% level, it is less than the city model. This is not surprising, since many of the suburban areas may not be included in the urban containment area of the MSA.

Overall, we find that levels of sprawl affect multifamily LQ levels most notably at the suburban level. This is most likely due to the relative growth suburban municipalities have experienced in the last three decades compared with central city areas. It is notable that the sprawl index coefficient has a much more substantial negative effect on multifamily LQ values in the suburbs than in central cities. The same is true of household size, which produces negative multifamily LQ results in both central cities and suburbs, but is much more substantial in suburban areas. The regional dummy variables produced few significant results, indicating that there may not be much explanation for the multifamily LQ values at the mega-regional level.

Figures 2 and 3 depict the relationship between the sprawl index and the multifamily housing LQ for MSAs with urban containment and those without urban containment. Most MSAs produce sprawl index variables between 40 and 80. The fitted line represents the relationship for both containment and no-containment areas and years. Figure 3 shows that 76 of the uncontained MSAs, or only about 58% of the MSAs, are above the fitted line denoting the TK. This indicates that MSAs with some type of urban containment policies do not necessarily produce higher-than-average multifamily LQ values.

Figures 4 and 5 show the relationship between the sprawl index and the multifamily LQ in all central city regions of all MSAs observed. The fitted regression line for both central cities with and without urban containment is not as steep as the regression line when all areas within the MSAs are examined, indicating less elasticity with regard to changes in the multifamily LQ within central cities. Again, most of the MSAs with containment have higher-than-average LQs for their respective sprawl index value. But there are fewer than in Figure 1, when the entire MSA was examined. This is expected, as the housing stock in central cities may not be influenced by growth containment as substantially as surrounding suburban areas. Central cities already tend to have a more diverse housing stock and more multifamily units.

Figure 1.

CBSAs and US regions studied.

Figure 2.

Scatter Plot of Multifamily LQ and Sprawl Index for MSAs with Urban Containment.

Figure 3.

Scatter Plot of Multifamily LQ and Sprawl Index for MSAs with no Urban Containment.

Figure 4.

Scatter Plot of Multifamily LQ and Sprawl Index for Central Cities with Urban Containment.

Figures 5 and 6 show the relationship between the sprawl index and the multifamily LQ within all suburban municipalities of the MSAs examined. Here, the fitted line resembles that of the entire MSA graph, but the distribution of suburban locations compared to entire MSAs points to a diverse suburban landscape with regard to multifamily LQ and sprawl index. This indicates that suburban areas experience a greater range of housing development by type. Some suburbs may develop densely, with high concentrations or multifamily units, while others may heavily favour the type of single family development that is commonly associated with suburbia in the USA. In fact, the highest multifamily LQ values can be found in suburban areas of MSAs. Those regions that are above a 2.5 LQ are the suburbs of Miami and Naples, Florida. The results for MSAs with containment also follow this trend. While most do produce higher-than-average LQs relative to their sprawl index variable, a great deal more appear below the fitted line compared with central city or total MSAs.

Figure 5.

Scatter Plot of Multifamily LQ and Sprawl Index for Central Cities with no Urban Containment.

Figure 6.

Scatter Plot of Multifmaily LQ and Sprawl Index for Suburbs with Urban Containment

Figure 7.

Scatter Plot of Multifmaily LQ and Sprawl Index for Suburbs with no Urban Containment.

Conclusion and policy implications

This study examined the relationship between urban containment, urban sprawl and several social and physical variables, and the rate of multifamily production within 202 MSAs as well as the central cities and suburban municipalities within those MSAs. The results of the random-effects GLS regression shows that a decrease in the sprawl index (less sprawl) is associated with an increase in the multifamily LQ, with a stronger association in suburban municipalities than in central cities. This may indicate that suburban municipalities in regions with less overall sprawl may feature an urban form that makes multifamily development more appropriate or more amenable, such as high residential densities or greater access to transit. Additionally, these municipalities may have implemented land use policies that make multifamily development easier to accomplish. As previous research has noted, most new multifamily development in the USA takes place in the suburbs (Atkinson-Palombo, 2010; Farris, 2001; Gober and Burns, 2002). As such it is important for planners and policymakers to consider these areas when developing or expanding urban containment boundaries.

Furthermore, the impact of urban containment policies was only significant at the 10% level in central city and suburban areas, but it was significant at the 1% level when applied to the entire MSA. This indicates that growth containment’s impact is more likely to be felt at a regional level, and that assessments of growth management should look beyond the borders of containment bounties for impacts elsewhere within a metropolitan area.

In addition, it is notable that the share of the elderly population has a negative relationship with the multifamily LQ. This relationship is more pronounced in suburban areas than in central city areas. There has been much discussion about where the large baby boom generation will choose to live after they retire, and what types of housing they will prefer. The life-cycle model of housing posits that older homeowners will seek smaller housing units once their children leave. But this has not been borne out by recent studies. Homeowners may have extensively modified their house to fit their tastes (Banks et al., 2010). Furthermore, Nelson (2009a) argues that boomers may be open to downsizing, but are not willing to leave their community, many of which are dominated by a single type of housing unit. Some have predicted that metropolitan areas will experience a ‘great inversion’ of movement to central cities (Ehrenhalt, 2013; Fishman, 2005). But there is little evidence that baby boomers are moving into central cities (Engelhardt, 2006; Frey, 2007). Households at retirement age are more likely to be receptive to smaller houses if they allow for decreased auto dependency, proximity to transit, and nearby work and retail (Colton and Collignon, 2001; Myers and Gearin, 2001). Recent surveys find that most baby boomers would like to age in the same state or metropolitan area where they currently live (Frey, 2007; Keenan, 2010). Reasons for this include the desire to maintain social connections and minimise the disruption and costs of relocation (Lawler, 2001; National Association of Area Agencies on Aging, 2011; Rosenthal, 2009).

This study indicates that older populations are more likely to live in regions, cities and suburbs with fewer multifamily units. Many older Americans would prefer to ‘age in place’ (Farber et al., 2011). As such, this study raises pertinent questions for planners and policymakers. It is possible that senior citizens would prefer to downsize, but are unable to because of a shortage of smaller units in their immediate area. This not only points to a potential undersupply of a multifamily units, but also the potential issue of senior citizens spending too much for housing that is underutilised. It is important to create elderly friendly communities in the places where these groups would prefer to live (Achenbaum, 2005). Nelson (2006b) and Myers and Ryu (2008) argue that zoning must be reformed to allow for more residential diversity to cater to the growing diversity of the suburban population. In addition to greater housing unit diversity, this would include smart growth goals such as neighbourhoods that include walkable destinations such as retail and parks. Furthermore, Nelson (2009a) and Binstock and Sykes (1993) argue that zoning and planning codes can be reformed that can allow for multigenerational and mixed-income housing. Additional research is needed on the desires of small suburban households and what they need and want.

In addition, the results for the poverty rate were significant in all three regressions and also worth noting. The poverty rate was negatively associated with multifamily LQ, and the effect was nearly twice as large in suburban areas as in central cities. This does not conform with the common perception that low-income households are more likely to live in multifamily units. This result may be due to the increase in suburban poverty rates over the past two decades (Berube and Kneebone, 2006). It may indicate that the population in poverty tends to live within a certain type of multifamily unit that this study was unable to detect (this study excluded structures with 2–4 units). Similar to the poverty rate, the result for household size produced a negative relationship. Again, this relationship was more pronounced in suburban areas than in central city areas. As with the poverty rate variable, the household size variables could indicate growing heterogeneity of suburban household and a potential housing mismatch (Larco, 2010). Not surprising was the negative relationship between homeownership and the multifamily LQ. There was little difference between the central cities and suburbs with regard to homeownership rates.

Increasingly, the current housing stock in many suburban areas does not meet the needs of retiring baby boomers or younger homeowners looking for starter homes (Capps, 2014; Ray, 2014). Suburban decline may be halted with densification and the modification of large-lot single family houses (Myers and Ryu, 2008). Resistance to multifamily developments is usually based on misperceptions or exaggerated fears of the impacts (Haughey, 2005). This can be overcome by identifying potential supporters, such as the direct beneficiaries, such as the developers, and construction workers, as well as local merchants and others who would benefit from greater pedestrian activity, as well as potential future project users (Micklow and Warner, 2014; Obrinsky and Stein, 2007). Other solutions include the implementation of floating zones, greater acceptance of PUDs, reduction of parking requirements or special use permits for multiunit development (Salkin, 2002).

While this study adds to the literature on sprawl, it does highlight an important limitation of assessing the impact of growth containment policies over a large number of different regions. The nature and geographic extent of urban containment policies in the USA varies widely between regions. Urban containment policies can include urban growth boundaries, the creation and maintenance of a greenbelt, or limits on services and utility provisions. Furthermore, the geographic area covered by the policy varies between each MSA as well. Some containment policies are restricted to the central city of the MSA, while others encompass the central city and surrounding suburban areas. Because this study examines the present impact of growth containment on entire regions, it was unable to account for differences in the type or coverage area coverage of growth containment policies. As such, future research should examine how the presence of ‘local’ growth containment will affect housing development patterns differently than ‘non-local’ containment policies.

The finding that MSAs that sprawl less have more multifamily units is not surprising, but it is notable that the relationship is more pronounced in suburban than in central city areas. In other words, it appears as if suburbs with more multifamily units, and possibly with a more diverse housing stock overall, are important aspects for achieving smart growth goals. Likewise, an increase in suburban poverty rates and an aging population indicates that there may be a present or looming housing mismatch between suburban municipalities and its residents. Suburban retrofits that increase the supply of multifamily units can also be a key smart growth initiative that can also reduce a potential mismatch between housing preferences and housing supply, as well as increase the supply of affordable housing. Multifamily units offer housing benefits to groups within the USA population that are growing, and planning for increased multifamily demand should not be a job just for planners working in central cities. The current literature on housing has not explored preferences in the suburbs in depth. We do not know about the preferences based on age segments, race, ethnicity, income and marital status to create communities that are suitable for a more diverse suburban population. Research should uncover what these boomers want and planners should plan for these demographic differences that support them (Bruin et al., 2011).

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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