Abstract
This paper examines the influence that the intrametropolitan growth in special districts has on residential property values. Our empirical approach tests whether the benefits of decentralizing local public good providers increases, decreases or leaves residential property appreciation rates unchanged. Past research in this area has been limited by the lack of variation in government structure within a region and by the self-selection of areas that decentralize governments. This research overcomes these limitations by 1) comparing appreciation rates for single-family homes that were located in areas that added local governments to appreciation rates for properties that were not; and 2) employing an estimation technique that border matches repeat sales to control for the self-selection of government structure. Overall, empirical results indicate that institutional decentralization has no influence on single-family property appreciation rates. It makes no difference whether the new government is the 3rd, 4th, 5th or 6th new jurisdiction–the new government does not influence appreciation rates. Residential property values for homes located in jurisdictions that added security special districts experienced rates of appreciation that were lower than otherwise comparable properties. Recreation, fire, water, sewer and other special districts had no measurable influence on appreciation rates. Empirical results also indicate that more overlap among local governments reduces appreciation rates. New governments created in areas whose residents have greater income heterogeneity increase appreciation rates. The distance separating the new government from existing governments, the land area of the new government and the creation of multiple new governments have no influence on appreciation rates. Finally, these results depend on the border matching repeat sales estimation technique employed here.
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Notes
Formally, this is termed, institutional decentralization, which is the shifting of governmental responsibilities to an additional government with associated taxing and spending powers. There is some debate over this terminology. We think about institutional decentralization as the creation of additional layers of government and use institutional decentralization and structural decentralization interchangeably.
Other special district services include ambulance services, flood control, irrigation, medical, mosquito control, pest control, storm drainage, street, television, transportation, and weed control.
Government refers to the public institution which has taxing and spending powers. Jurisdiction refers to the physical area served by a government.
Fiscal decentralization is the spreading of tax and expenditure responsibilities to lower tiers of government.
Empirical papers using Census of Government data cannot distinguish between these two government structures within a metropolitan area. In 2002, the Census of Governments added spatial information. This data distinguishes whether a jurisdiction overlaps multiple counties, overlaps a city, and if a jurisdiction is contained within a county. This information describes the general distribution of governments, but does not distinguish the amount of overlap between governments.
In some cases, overlapping governments provide the same category of functions, but they are qualitatively different. An example is the county provision of highway police and an overlapping city’s provision of urban police services.
As noted by an anonymous referee, the efficiency gains of a new special district (SD) may be long-run in nature. The longer term unanticipated public services/taxes at formation are not a large concern given that the average time between the formation of a SD and the second sale is 4.91 years in our data. This is sufficient time for the SD to establish itself and provide regular services and taxation.
We test the validity of this assumption.
A-priori, there is no reason to expect changes in structural characteristics to be related to changes in government structure. Allen Goodman observed that homeowners can implement changes in structural characteristics themselves but need governments to provide local public goods and to deal with externalities. An alternative to public sector controls for land use externalities and the provision of LPGs are land use covenants and homeowners’ associations. As noted by Hughes and Turnbull (1996), land use covenants provide a Coasian mechanism to deal with land use externalities and increase property values. As discussed in McKenie (1994), homeowners’ associations provide a similar role in LPG provision.
Holmes (1998) uses border matching to examine the impact that right-to-work laws have on firm location; Black (1999) border matches single-family homes to estimate how school quality is capitalized in house prices; and Billings (2009) border matches employers to examine the impact that enterprise zones have on the location of jobs. Ries and Somerville (2004) examine whether school quality is capitalized in Vancouver house prices by border matching repeat sales after the local school district changed elementary and secondary school attendance zone boundaries.
Only five of the six types of special districts will serve a property given the redundancy of water, sewer, and water-sewer special districts.
Security can include security gates, lighting, security guards, signage (e.g. traffic calming as well as neighborhood watches).
Other services for Metropolitan SDs include ambulance services, flood control, irrigation, medical, mosquito control, pest control, storm drainage, street, television, transportation, and weed control.
These variables are all constructed in ArcGIS 9.0 based on spatially encoded local governments maps from the State of Colorado.
In cases where a new government overlaps more than one government, this metric becomes the average overlap for all pairwise combinations of overlapping governments.
The median household income for residents located in the jurisdiction served by the new government is computed by aggregating all Census block groups within the new jurisdiction. The inclusion of partial block groups is based on proportion of shared land area between a given block group and the new government.
Brasington (2001) also finds that the amount of capitalization is negatively related to the size of a jurisdiction. This measure controls for any capitalization differences due to the size of a jurisdiction.
As discussed by Olson (1969), one expects residents to set jurisdictional boundaries in a way to internalize most of the benefits of a new government. Therefore, spillovers would be small in magnitude.
Private governments (e.g. homeowner’s associations and gated communities) are not included in this data. Border matching repeat sales will remove the influence of private governments because they are typically created in conjunction with a new development. Also, since private governments represent direct substitutes for special districts, there are limited benefits from forming special districts that have coterminous boundaries with private governments. In the end, any pre-existing private governments will be capitalized in sales prices transacted before the formation of a special district.
These files exclude special districts considered nonactive by the state of Colorado and those with inaccurate jurisdictional maps.
Denver and Broomfield are both cities and counties, integrated into a single government institution and treated as a county in this dataset. The Denver-Boulder-Greeley CMSA consists of Adams, Arapahoe, Boulder, Broomfield, Denver, Douglas, Jefferson, and Weld counties.
Of these 467 special districts, 203 are classified as metropolitan districts (special districts providing two or more services), 83 as water-sewer only, 80 as fire only, 42 as water, 33 as sewer, and 26 as recreation. Among the 203 metropolitan districts, 133 provided security, 175 water/sewer, 149 recreation, 21 fire, and 183 provided other services. This total number of special districts excludes some special districts with missing or limited data. In some cases, paper maps were used to update or supplement jurisdictional information. Our analysis requires transactions two years prior to the formation of the new government. Consequently, special districts created to just finance infrastructure for new residential developments are excluded.
This data was accessed from the Property Database Center website, http://www.myPDC.com, beginning 2/15/07.
The average land area of new governments was about 4.6 square miles. This research adopts 1/2 mile distances to new government boundaries and one square mile neighborhoods (just under 20% of the average new government area) over smaller geographies for two reasons. First, restricting the data to smaller distances reduces both the number of observations and the number of new governments used to estimate parameters. Second, smaller neighborhoods (e.g. 1/2, 1/4 square mile) assume that changes in neighborhood characteristics are localized in impacts, when in fact, factors such as crime and school quality likely influence larger geographic areas. As a sensitivity test, we examined 1/2 square mile neighborhoods and 1/4 mile buffers. This yielded 6,888 observations and produced results that are broadly similar to those reported here.
One limitation of this data is the lack of detailed expenditure categories within multiple function special districts. This shortcoming is mitigated with the availability of the functions provided within these special districts and measures of total expenditures. Variation in functions and expenditures allow identification of functional and expenditure impacts within a multiple purpose special district.
Most of the 151 excluded special districts were created to finance infrastructure for new residential developments.
State laws regarding formation of SDs and governance structure vary among states.
This value is chosen because the average annual appreciation rate for all repeat sales in this data was 5.8% with a standard deviation of 8.8%. Therefore, annual appreciation rates exceeding 40% are about 3.9 standard deviations away from the mean. The border matched repeat sales have a 6.8% mean rate of appreciation and a standard deviation of 4.3%. The Office of Federal Housing Enterprise Oversight, http://www.ofheo.gov/hpi_download.aspx, reports annual average rates of house price appreciation over the 1987 through 2004 period of 5.3% for Denver; 6.3% for Boulder; and 5.1% for Greeley. The Case-Shiller Home Price Index for Denver reports a 5.39% annual rate of appreciation over the 1987–2004 period, http://www2.standardandpoors.com/spf/pdf/index/ CSHomePrice_History_072943.xls.
Goetzmann and Spiegel (1995) used house price quartiles.
A home buyer is unlikely to have information about a new government that will be formed more than two years into the future. Given the state statute for Special Districts dictates the approval timeline, two years is sufficient time to ensure that the anticipation of special district formation is unlikely.
An example is matching a property just inside the western border of a jurisdiction with another property just outside the eastern border of the same jurisdiction. For large jurisdictions, this would involve border matching over a much larger distance than 1/2 of a mile.
Security services may push crime to neighboring areas, but this would generate a negative spillover and only reinforces the negative estimated impact of security services.
This required a slightly different repeat sales specification. For example, to test whether the hedonic coefficients for properties located in recreation special districts were constant over time, the repeat sales equation included two dummy variables: one that equaled one if the property was located in a recreation special district at the first sale (and was zero otherwise); and a second dummy variable that equaled one if the property was located in a recreation special district at the time of the second sale (and was zero otherwise). The F-test statistics reported in Table 9 test the equivalence of these two estimated coefficients.
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Acknowledgements
This research has benefited from comments by Richard Arnott, Sanjay Bhagat, David Brasington, Jan Brueckner, Charles de Bartolome, Allen Goodman, Robert Inman, Randy Walsh and Jeffrey Zax. Christine Martell’s work with special districts in Colorado helped motivate this research. All errors are our own.
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Billings, S., Thibodeau, T.G. Intrametropolitan Decentralization: Is Government Structure Capitalized in Residential Property Values?. J Real Estate Finan Econ 42, 416–450 (2011). https://doi.org/10.1007/s11146-009-9205-z
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DOI: https://doi.org/10.1007/s11146-009-9205-z