internalizing a good externality

7/30/2019 Internalizing a Good Externality

1/32

Doing Well by Internalizing a Good Externality 1

Doing well by internalizing a good externality: Combining House Price Capitalization

with Altruistic Strategies

Brock Lacy

London School of Economics and Political Science


2/32


Abstract

Could a developers strategy of discounting land for a new public school increase

their profits? This papers hypothesis is that by wielding an understanding of house

price capitalization of schools, a developer could increase the price of their houses

above the costs of the discounting of said land. Thus a developer could do well by

doing good. This is then a study of the use of an internalization of an externality

strategy. In 2006, a new school location was announced in a rural county in Utah

State. The price of the school site was discounted by the developer. Using historical

sold house price data collected from the Multiple Listing Services the house price

capitalization of the new school was tested. The method used to test the

capitalization rate was a difference in differences analysis; with the unaffected

portion of the county as the control group. Once the capitalization rate was found the

profit of the internalization strategy would be estimated by weighing the cost of the

discount to the developer against the projected capitalization across their

subdivisions. The analysis finds a zero capitalization of the announced school into

house prices, rendering the profit estimation unnecessary. It is set forth that this may

be due to the plentiful developable land in the treatment group. Therefore it is argued

that in areas with less developable land, such a strategy may indeed lead to increased

profits. Further research into this strategy is discussed and recommended.

Keywords: capitalization, internalization, strategy


3/32


Table of Contents

Introduction 4

Literature Review 6

Data 13

Method 15

Results 23

Conclusions 27

References 29


4/32


5/32


The analysis will be focused on a rural community in Utah State; this community can be

characterized as a fast growing community not too distant from the central business district

(CBD). The community in question had out grown the only elementary school in the whole of

the school district and thus the districts school board deemed it necessary to construct a new

school. A developer with two large subdivisions in this community deeply discounted the land

for the school site. This then leads to a situation where the above strategy question could be

answered. Could the strategy of doing good, by discounting the price of the land for the school,

raise the house prices enough where this seemingly altruistic action actually turned a profit? To

the extent that the confluence of house price capitalization of the announced new school and the

decision to discount the schools building lot is found to be profitable this paper will then attempt

to make a recommendation concerning the usefulness of such a strategy.

The analysis will be derived from sold house prices obtained from the multiple listing service

(MLS). The method used to estimate the effect of the new school announcement on house prices

will be a difference in differences analysis. Once the capitalization of the new school is found it

will then be used against the cost of the discount from the developer to find the profit of the

discounting of the land. The fitness of the control group to the treatment group was found to be

balanced using the propensity score matching (PSM) technique. The window of analysis will be

three years prior to the announcement and three post the school location announcement. The

difference in differences estimator renders an estimate of 0.8 percent capitalization of the new

school into house prices. However the estimation is found to be statistically insignificant, thus

rendering the next steps of the profit estimation useless. The zero capitalization of the new

school is hypothesized to have occurred due to potentially a number of reasons: uncertainty,

limited data, time lags, and a plentiful supply of developable land. Although the results from the


6/32


analysis renders a zero capitalization of the new school into house prices and thus could suggest

that this strategy should not be recommended, there are limitations which throw doubt onto such

a conclusion. Thus this paper will recommend further study of such a strategy and discuss the

potential scenarios in which this strategy could be profitable. This paper will be outlined as

follows I) Introduction, II) Literature Review, III) Data, IV) Method, V) Results, VI)

Conclusions.

II) Literature Review

No discussion of house price capitalization of schools would be complete without mention of the

influential paper by Oates (1969). While pursuing evidence of the Tiebout (1956) hypotheses,

Oates made the following argument:

[T]here is some reason to believe that the Tiebout hypothesis may be relevant to the real

world:Individuals working in a central city frequently have a wide choice of suburban

communities in which to reside, and the quality of the local public schools, for instance,

may be of real importance in the choice of a community of residence. If this is true, the

outputs of public services (as well as taxes) should influence the attraction of a

community to potential residents and should thereby affect local property values. (p. 958)

In his subsequent analysis Oates found that per pupil expenditure has the effect of increasing

house prices, while property taxes decrease house prices. Oates argued that this is evidence that

the Tiebout hypothesis is operative, the true meaning of Oates findings were clarified by


7/32


Chaudry-Shah (1988) .. the Tiebout mechanism of foot-voting does operate, but it does not

imply that the local public services are provided efficiently (p.210). Although it is widely held

that Oates research is not the conclusive evidence of the Tiebout hypothesis he sought, his paper

has helped to spur the investigation of the capitalization of all types of externalities into house

prices, and is one of the first steps toward an empirical understanding of house price

capitalization.

There has been ample research since the Oates paper regarding the capitalization of schools. The

majority of papers find that schools have an important impact on house prices. This paper will

highlight a few of this research but would like to note that the papers heretofore mentioned

represent but a small portion of the empirical work.

The majority of house price capitalization of school research is predicated off hedonic analysis.

Hedonic analysis is especially sensitive to omitted variable bias, thus much of the past analysis

used methods and models that attempt to limit this bias. One such method was developed by

Black (1999) wherein she used what is called Boundary Fixed Effects (BFE). The basic idea is

to find houses that are sold on either side of the school catchment boundary, thus it is argued that

these houses are in the same neighborhood and subsequent neighborhood quality is then

controlled for. Using this technique she finds that a 5 percent increase in test scores leads to a

2.1 percent increase in house values. She checks the robustness of her results by making the

analysis area around the catchment boundary smaller and smaller and finds that her results do not

change significantly. The BFE method has been used by others in varying degrees to test the

house price capitalization of school quality (Bayer, Ferreira, & McMillan, 2007; Clapp, Nanda,

& Ross, 2008; and Dhar & Ross 2010). However, there are some who argue that BFE may not


8/32


be a sufficient control for neighborhood quality. Gibbons, Machin, and Silva (2009) illustrate a

potential failure of the BFE technique with the following :

[A] situation could arise if, for example, one attendance zone contained a rail station and

another did not. This would then result in higher prices, richer families and better

schools in the station zone, and a spatial trend in house prices rising across the

boundary towards the station..Hence we could find a correlation between house prices

and school quality amongst closely space neighbors that is not caused by the demand for

school quality, but a residential sorting that is a consequence of demand for rail access.

(p. 9)

This potential bias is echoed by Kane, Riegg, and Staiger (2006) as they stated, to the extent

that this sorting occurs, it will bias boundary estimates toward finding a positive association

between school quality and property value, unless one fully controls for these differences across

boundaries (p. 194). Nguyen-Hoang and Yinger (2011) discuss one more potential bias of the

BFE approach. This was the uncertainty argument put forth by Cheshire and Sheppard (2004)

and Zahirovic-Herbert and Turnbull (2008); which is that households living close to a boundary

place a lower weight on across-zone boundaries because they believe that these boundaries might

change, as they occasionally do (p. 33). Cheshire and Sheppard (2004) take it a step further and

postulate that the potential for uncertainty could be strongest in the peripheral areas with

relatively more new construction.

Additionally, there are some derivations of BFE that have been used in past research as well. In

particular importance to this paper is the difference in differences technique. Difference in

differences has been used in a myriad of ways. Recently it has been employed to gauge the


9/32


effect of changing school quality via change in school catchment zones. In this way the

difference in differences estimator can be used to find the capitalization of school quality in

house prices. One of the first uses of difference in differences to estimate school quality

capitalization was Bogart and Cromwell (2000). They examine the changing of catchment

boundaries in Shaker Heights, Ohio in 1987. One of the reasons for the change was to integrate

more of the poor minority students with the richer white students. Bogart and Cromwell (2000)

argued that the change in the boundaries could have two effects. The first of which was the

neighborhood schools effect (Bogart and Cromwell, 2000, p. 281) wherein if the boundary

change resulted in the loss of a local school then parents might find it more difficult to be

involved and potentially make it harder for students to be involved with after school activities

which effects they argued, could damage the quality of the school. The second was the racial

composition effect. (Bogart & Cromwell, 2000, p. 281) This effect would possibly drive house

prices down if there were prejudice in the community. In their analysis they use standard

house structure variables such as age of house, lot size, and living area. They include third grade

reading test scores as the variable for school quality. The difference in differences estimation of

school quality and the related theoretical effects were all the more meaningful as only elementary

catchment boundaries were changed, while all students attend the same high school. This in

theory controlled for omitted high school changes that could also affect house prices, the

importance of which was discussed by Nguyen-Hoang and Yinger (2011). Bogart and Cromwell

(2000) attempt to control for omitted variable bias by including variables for neighborhoods;

percent nonwhite and house construction quality grade. Using a log linear OLS they find that

their difference in differences estimator of the effect of the change in catchment boundaries

decreased house prices by 9.9 percent.


10/32


In a similar paper Ries and Somerville (2010) examine the effects of changing school catchment

zones on house prices. Their area of study was Vancouver, British Columbia, Canada. In

September of 2000, the Vancouver School Board announced plans to alter school boundaries,

which affected 20 percent of houses. Ries and Somerville (2010) argued that examining school

quality in Vancouver had advantages: there is a single tax rate and a more standardized level of

municipal services (p. 930) and the racial issues that so pervade location decisions in the

United States are not as present (p. 930). They added house preference and location are

somewhat unbundled (p. 930) this they argued is evidenced by the small percentage of

observations from condominiums (p 930). Ries and Somerville (2010) found that when a repeat

sales index is used as opposed to the cross-sectional hedonic regression, bias from omitted

variables is dampened. They discover that the school boundary change had little to no effect for

all groups except those houses in the highest quartile price range. This result echoes Chiodo,

Hernandez-Murillo, and Owyang (2010) and Cheshire and Shepherd (2004). Interestingly

enough the effect of changing an elementary school was statistically insignificant as the analysis

became more sophisticated (moving from cross sectional hedonic analysis to repeat sales index).

There has been some research concerning the effect of school infrastructure investment on house

prices. The most salient to this paper, would be research conducted by Cellini, Ferreira, and

Rothstein (2010). Using the principle of house price capitalization, they employ regression

discontinuity design to find the effect of school facility investments on house prices. They

contend that there is good reason to expect school facility investment to have a positive effect on

house prices. Beyond the potential effect of improved outputs (higher marks, grades,

graduation), Cellini et al (2010) reasoned that capital investments may also lead to

enhancements in student safety, athletic, and art training, the aesthetic appeal of the campus, or


11/32


any number of other nonacademic outputs (p. 222). Using the passage of closely contested

school facility investment bonds in California their analysis finds the approval of said bonds

caused house prices in a district to rise by about 6 percent. Cellini et al (2010) argued that this

effect appears gradually over the two or three years following the elections and persists for at

least a decade (p. 218). Cellini et al (2010) submit that the effect of school facility investment

on house prices is gradual due to construction lags and school quality signals. Thus the full

effect of the new school or school investment on house prices and property could take some time

and therefore build momentum as time progresses.

Research concerning capitalization of externalities (e.g. house price capitalization of schools) has

potential for recommendation of business strategy; to this end there has been some academic

research. One such research paper was authored by Pashigian and Gould (1998). They

hypothesized that anchor tenants in shopping malls drive foot traffic. This increase demand for

access to these anchor tenants could increase the sales of non-anchor tenants. Thus they argue

that non-anchors could free ride off the success of their anchor tenants. Pashigian and Gould

(1998) theorize that developers could internalize this anchor externality by charging less rent to

anchors and more rent to a non-anchor tenant; in a sense it is a rent premium for non-anchors to

access traffic driven by the anchor. Using a tri-year survey conducted by the Urban Land

Institute they found that anchor tenants paid 72 percent less than their non-anchor neighbors. As

Pashigian and Gould (1998) interpret their results [t]hose externalities are internalized by the

developer through store rents. Our evidence indicates that the external economics created by

anchors are reflected in the lower store rents paid by anchors (p. 140). They further clarify the

strategy of discounting rent for anchors relative to non-anchors by developers as rational

because they know that anchors attract customers to the mall and increase the sales of other


12/32


mall stores (p. 140) A similar result is found in Benjamin, Boyle, and Sirmans (1992), wherein

they find that rent falls as shopping center stores sales increase. Brueckner (1993) examines a

similar shopping center strategy for developers. His analysis is concerned with inter-store

externalities, similar to the Pashigian and Gould (1998) analysis. In particular he is concerned

with the size of stores within shopping centers and the effect of store size on the sales of other

stores within the shopping center. He developed a model that claims that a profit maximizing

developer will choose store sizes that make the shopping center more attractive and thus increase

the inter store externalities for all of the shopping center.

The above surveyed literature concerning the strategy of internalizing externalities have been

constrained to the non-residential real estate market. This is due to the dearth of academic

research regarding residential internalization of externalities strategies. However one paper that

is significant to this essay is from Thorsnes (1998). He was interested in a strategy which would

lead to a greater capitalization of local amenities. He reasons that a larger subdivision should

lead to greater capitalization of said amenities. He used sales of building lots to test his

hypotheses. Thorsnes (1998) explains that the advantage of sold building lots over sold houses

are twofold the value of amenities is capitalized into the sale price of the building lots and the

many characteristics of the house need not be controlled for (p. 398). The analysis of these

observations renders the following result: that adding one additional acre to the median

subdivision would increase the sales price of a building lot by 3 percent.

A seemingly tangential bit of literature is appropriate to note here. Hilber and Mayer (2009)

asked: Why childless households would support increases in school spending? (p. 74). They

argued that per pupil expenditure is more positively capitalized in those areas with less

developable land.(p.74). Or in other words, areas which are seemingly characterized by more


13/32


inelastic supply of housing have higher capitalization of schools than more elastic areas. This is

potentially of crucial importance to this papers analysis as the area in which the treatment

occurred had plenty of developable land and thus one could argue was relatively characterized by

elastic supply.

Theory and past research could with reason lead to the fundamental question of this paper: Could

the discounting of land for a new school be profitable for a developer? This paper submits that

in theory the answer to this question should be in the affirmative. That is there is good reason to

expect that under the right circumstances a potentially altruistic act could in fact be economically

rational.

III) Data

The data used for this analysis was collected from the MLS. The MLS is an internet site that

lists prices for houses but also carries historical sold house prices for given regions or cities.

Prior to treatment date and time period selection the data included over 750 observations from

both the treatment and the control area. These observations ranged from 2001 to 2012.

Nevertheless this kind of data is not without potential problems. Parmeter and Pope (2009)

described one of the weaknesses of MLS data by the following:

[R]ealtors are typically not required to fill in all of the fields within the MLS system. For

example, some realtors may consistently input information on the type of flooring in the

house whereas others may not. Furthermore, it is likely that realtors are selective on the


14/32


15/32


IV) Method

The lynchpin of difference in differences analysis is the correct selection of the treatment and the

control group. Similarly to Bogart and Cromwell (2000) all students in both groups attend the

same high school after the school announcement; thus controlling for the potential bias of the

effect of differing high school quality. The treatment area was confined to Mountain Green and

Peterson Utah, while the control group contains all other cities in the school district (Morgan,

Milton, and Croydon). Table I illustrates some demographics via the US Census Bureau (2010a)

(2010b). The Mountain Green area is an unincorporated city located 35 miles northwest of the

CBD. Mountain Green is characterized as a rural area in a mountainous canyon close to ski and

snow resorts. According to the US Census Bureau (2010a) Mountain Green had a population of

2,309. Although the city is relatively small it is rather prosperous with a median household

income in 2010 of 95,263 dollars (US Census Bureau, 2010b). However, this statistic may be a

little misleading if one does not consider the interval in which this median household income is

derived. The US Census Bureau estimates at a 90 percent confidence interval with a margin of

error of 35,327 dollars. When considering other control areas this margin of error is

substantially larger. Thus although the measure of income in the treatment area is somewhat

larger than the commensurate number for the control area, it has a rather large range which needs

to be kept in mind when comparing the two areas.


16/32


Table I.Demographics*

Mountain Green Morgan City

Population 2,309 3,687

Median Household Income $ 95,263.00 $ 60,781.00

White 98.90% 99.5%

Mean House Price 2003 -2009 $ 358,223.79 $ 253,653.15

Industry Employed:

Agriculture 2.90% 2.00%

Construction 14.90% 11.70%

Manufacturing 12.00% 14.20%

Wholesale Trade 1.40% 3.40%

Retail Trade 8.80% 15.20%

Transportation 10.70% 2.80%

Information 0.00% 0.60%

Finance, Insurance, Real Estate 4.90% 6.20%

Professional 7.10% 2.70%

Education 16.90% 20.20%

Arts 9.50% 4.30%

Other 0.80% 3.10%

Public Adminstration 10.00% 13.60%

* - US Census Bureau, 2006-2010 American Community Survey

The control area which will from here forth be referred to as Morgan City (although there are

few other smaller cities included in the control group there are only limited observations from

these cities and Morgan is by far the largest of these cities). Morgan City is the seat of the

Morgan County government and the Morgan School District. Morgan City is located


17/32


approximately 10 miles from the treatment area, Mountain Green. Referencing the US Census

(2010), it was estimated that Morgan City had a population of 3,687. Although Morgan has a

larger population than Mountain Green, its median household income is much smaller, as

estimated at 60,781 dollars (US Census Bureau, 2010b). This measure of centrality for Morgan

City has a much smaller range in the confidence interval as opposed to Mountain Green at only

plus or minus 12,891 dollars. Both measures of income are relatively higher than the Utah

States median income (56,330 dollars) (US Census Bureau,2010c). In theory one would expect

the effect of schools to be more highly capitalized in neighborhoods with higher income (such as

Mountain Green) as per the findings of Cheshire and Sheppard (2004) and Chiodo et al (2010).

Precedence for using the non-affected area of the school district as the control group was set

forth by Bogart and Cromwell (2000) and Ries and Somerville (2010). Bogart and Cromwells

(2000) treatment and control group are located in the same school district and prior to the

announcement of the policy attended the same elementary school. Similarly to Bogart and

Cromwell (2010) all students in both groups attend the same high school after the policy

announcement; thus controlling for the potential bias of the effect of differing high school

quality. The selection of the control group was also influenced by Cellini et al (2011). The

preliminary reasoning was that a new elementary school in the Morgan County School district

represented two events 1) a potential change in school catchment boundaries and 2) a new school

structure. Thus the use of Morgan City as the control group seemed logical as the boundaries

were affected, but the students in Morgan would not be changing or losing their school, thus they

would be unaffected. In addition the proximity of the two areas to one another, the relative

shared amenities, the approximately same distance to the CBD, the unincorporated nature of

Mountain Green (in a very real sense this meant that the county was the political power over


18/32


19/32


the propensity scores of the treated and the control groups are balance. To insure whether the

control group and treatment group do match according to the PSM, the data was analyzed and

was found that the matching principle was satisfied via STATA.

There is repeated numerous times throughout house price capitalization literature the importance

of controlling for neighborhood quality, especially for BFE analysis and its derivations. As the

treatment area and the control area are relatively small in population and they share some

important characteristics, (e.g. high school and elementary school prior to the school

announcement, distance to CBD, terrain, and size) it seemed reasonable to view the two analysis

areas as neighborhoods within in the school district. One more neighborhood variable which

will be included from the data is sold houses in the developers subdivisions. Upon examination

of the results, it is shown that these subdivisions command a premium and increase the

robustness of the model used.

Difference in differences analysis is an attempt to find the causal effect of policy change or an

event on a dependent variable. As such the analysis used the following log linear equation:

Ln(V) = X + Z + W +

This regression was ran using OLS to find the difference in differences estimator W, meaning

post treatment date and treated area; while Z is the dummy variable for post treatment but not

controlled for treatment group. V is the sold house price. X is a vector of both structural

characteristics and neighborhood characteristics including dummy variables for year sold. An

additionally crucial point for estimation of the effect of the school on house prices is to identify

the treatment period or event. As this discussion pivots on when the market knew the school

would be a reality in Mountain Green. The researcher was able to identify potentially three


20/32


different signals to the market that the school would indeed be located in Mountain Green.

These three are: 1) the announcement of the land purchase at a discount by the school district, 2)

the passing of the school bond, and 3) the announcement of the decision regarding where to

locate the school. First the announcement of the land purchase at a discount. Although the exact

date of the land purchased is not publically known, it is within reason to assume that it became

publicly known on July 1st 2007. The day in which the local newspaper published an article

discussing the purchase but not noting the date; however the article in question did discuss the

discount of the land for the school structure. According to the newspaper article, the company

building the expansive subdivision around the new elementary school, sold the 10-acre school

site for a very reasonable price of about $15,000 per acre, or almost $150,000 (Winterton,

2007, p. 4B).

The second potential treatment date may have been the passing of the bond for the new

elementary school. The voting regarding the bond was to be held in early November 2006.

Prior to the successful passing of the bond, there were two previous attempts to secure funding

for a new elementary school in Mountain Green. In both 1999 and 2000 the Morgan School

District had posted referenda for the school bond, however both times the bond for the

construction of a new school was defeated by the slimmest of margins (both times the bond

failed by less than 12 votes) (Winterton, 2006a). The impotence for this bond was the dire need

for bigger facilities. As one journalist noted, While Morgan Elementary's enrollment hovers at

800, the average elementary enrollments in surrounding school districts are much lower

(Winterton, 2005). Another compelling argument for the use of the bond passing as the

treatment date concerns the markets ability to predict its success. This is evidenced by the

failures of two bonds seven years early explicitly concerning the new elementary school in


21/32


Mountain Green. This gives credence for the bond passing as a potentially unanticipated event

and a logical position for the treatment period.

The last potential treatment period considered was the announcement of the school location. In a

school board meeting on January 10th

2006 the school board announced its desire to build in the

developers subdivision (Winterton, 2006b). Although the final decision for the location of the

school site was not decided at the meeting, it was later decided that it would be built in the

subdivision.

As one could imagine all of the treatment periods listed above were not without deficiencies.

The announcement of the school discount via the newspaper article does not give a date for

which the purchase was completed. Additionally it is hard to argue that this in anyway should

effect the price of houses within the treatment area; one could conceivably argue that this was a

signal to the market of the developers commitment to the school, but if that were the case then

the analysis would measure the effect of the developers commitment to the school not the value

of the school. This would in theory measure the marketing value of the discount. This date was

rejected as the treatment date. The school bond is seemingly the best treatment date, however

there are some glaring holes upon further examination. Perhaps the biggest of these holes is the

potential leakage of the passing of the bond into the control group. The bond was not only for

the construction of a new elementary school in Mountain Green but also for substantial upgrades

and renovation for the control groups elementary school and high school. It is within reason to

assume that the treatment group and the control group could potentially be affected by the bond

passing, and the treatment date is analyzing more than just the effect of the new elementary

school but of the investment in the high school if a different control group were to be used. It is


22/32


with this reasoning that the announcement of the school site was used as the treatment date. It

had been known prior to the announcement of the selection of the school site that the school

board was deliberating certain locations, even to the point where the location was narrowed

down to a few locations including the final location prior to the final announcement. However, it

is the cleanest of the three treatment dates. It is the earliest concrete announcement from the

school board concerning the new elementary school and its location. It only concerns the effect

of the new elementary school on the treatment area with potentially little to no leakage to the

control group. The selection of the announcement of school location is not perfect either. As

mentioned earlier there were discussions by the school board concerning the site of the proposed

elementary school prior to this announcement and thus the market knew that there was a

deliberation. However, as far as this researcher could gather there was no deadline for selection

and no indication of when the decision would be finalized. This gives rise to the argument that

the effect of the new elementary school would not be capitalized prior to this date.

The final proposed step would be to project the profit from the discounting of the land for the

developer. This would include taking the capitalization of the new school from the difference in

differences estimator of the new school and then projecting that onto vacant lot prices into the

future. Unsold lots would be discounted at the market capitalization rate and at the average time

to sell provided by the developer. However, as the results from the analysis indicate a zero

capitalization rate this step will not be pursued.


23/32


IV) Results

Before running the analysis the data was observed to contained some variance that if not

smoothed out, could likely cause bias. It is to this end that outliers were removed to make the

data more manageable. The results of the difference in differences analysis are found in Table II.

These results were obtained by running the OLS regression robustly through STATA software.

The results seem to have an overall good measure of fit with an adjusted R-Square of 0.8465. To

further strengthen the argument of the model as a good fit, all the results have the correct signs.

What is more, all of the structural and neighborhood characteristics are statistically significant.

A home buyer would pay a premium for additional acreage, bathrooms, bedrooms, and to finish

more of the basement. On the contrary the sign of the age of the house is negative and

statistically significant. Additionally one would pay a

premium to buy a home in the Mountain Green treatment area as opposed to the Morgan City

control area. To illustrate one would have to pay approximately 10 percent more for the same

house in Mountain Green. The result is similar (and expected) for the developers subdivision.

One would have to pay approximately an additional 6.1 percent to buy the same house in any of

the developers subdivisions. However the statistical significance of this result is minimum at 20

percent level of significance. This may be due to the relatively few observations permitted from

the MLS. Given the quality of the subdivision one would suspect that this coefficient should

indeed be significant. Additionally the results indicate that the there was a premium for all

houses sold after the announcement of the school location. This result is not statistically

significant from zero. Although the year dummy variables were not included in the results


24/32


presented in Table II, they were included in the regression, the results of which have the

expected signs but have varying degrees of significance.

Table II

Difference in Differences Results

VariableCoefficient

(standard error)

ln acres 0.1376***(0.0121)

percent basement finished 0.0444***(0.0199)

total bathrooms 0.0264***(0.0114)

total square feet 0.0001***(0.0000)

age of house -0.0119***(0.0009)

age of house squared 0.0001***(0.0000)

Developer's Subdivison0.0611*(0.0449)

Treatment Area (Mountain Green and Peterson) 0.1007***(0.0313)

Post School Site Announcement 0.0196(0.1503)

Post School Site Announcement and TreatmentArea 0.0084

(0.0361)

R - Squared 0.8465

* - 20% level of signficance

*** - 5% level of signficance


25/32


Turning to the results of the differences estimator, the coefficient has the sign that would be

predicted by theory. Just like Cellini et al (2010) the announcement of the school location has a

positive effect on house prices. However, the results are statistically insignificant from zero.

Furthermore this result should not be considered unusual as the results are similar to Ries and

Somerville (2010), with less data and without the benefit of repeat sales index. Although this

prevents the implementation of the proposed methodology to test the profitability of the

discounting land for the school strategy, it does beg the question: Why would the results be

insignificant from zero? Referencing the work of Chiodo et al (2010), Cheshire and Sheppard

(2004), and Ries and Somerville (2010) it could be expected that due to the higher house prices

in Mountain Green coupled with the higher median income, that there would be rather strong

capitalization of the new school. However, there are copious potentials for why it was an

essentially zero capitalization. One reason is that the control group and the treatment group may

not be matched. One must note the limitations of PSM. PSM requires a large data sample. It

could be argued that this was requirement was not met with the observations available from the

MLS. Additionally there are a few theoretical reasons why there would be capitalization of the

announcement of the new school location that was statistically insignificant from zero. There are

more explanations not discussed here, this paper will try to outline a few the researcher believes

are most salient. They are: 1) uncertainty, 2) limited data, 3) time lags and 4) plentiful

developable land.

First, the uncertainty proposal is championed by Cheshire and Sheppard (2004). From their

analysis they argue that [s]chool quality also appears to be significantly and additionally

discounted in areas in which new construction is concentrated. They continue, it reflects

uncertainty as to future changes in school catchment areas in such neighbourhoods and so


26/32


uncertainty as to what school an address will in future be assigned. This argument is

strengthened when the projections of growth in the whole of Morgan County. As projected by

the Utah Governors Office of Planning and Budget (2008), Morgan County will experience an

average annual growth rate of 3.8 percent; making Morgan County the second fastest growing

county in the state. This kind of projected growth could lead to uncertainty concerning the

schools boundaries and quality. Second, the data used to perform the analysis was rather

limited and therefore could cause some questions concerning the legitimacy of any conclusions.

It is within reason to assume that with more observations and with more variables one could

draw more robust conclusions, however this analysis did not have that privilege and was left to

suffice as best as possible. Thirdly, Cellini et al (2010) found that it took three to four years for

the increase in house prices from the passage of the school bond. Furthermore it could take time

for the value of the school to grow and signal its quality to the market. Fourth, the area both in

Mountain Green and in Morgan City could be characterized as rural, with plenty of developable

land or could be characterized as having elastic supply. This could affect the capitalization as

Hilber and Mayer (2009) observed. With their results in mind it could be expected that in

regions with plenty of developable land that the capitalization of schools into house prices could

be zero. This is potentially due to the ease at which other home owners can access the school

without increasing their bids accordingly. Although all of these arguments potentially have merit

it is the elastic supply argument that is the most salient for this area. However,, it should be

stressed that none of the other arguments are rejected as potential explanations.


27/32


VI) Conclusions

Will discounting land for a new school be profitable for a developer? The answer is similar to so

many other answers in economics: it depends. If the explanation that elastic supply is driving the

zero price capitalization of the new school is believed, then it may increase house prices if the

supply in the area that will allow access to that new school is limited, one could then expect a

higher capitalization rate. However, if the development is located in an area like the treatment

area than one might expect a zero capitalization of that new school; does this mean that in more

rural areas with more developable land it is not profitable to discount land for a new school? The

answer is unclear. However, there may be other reasons to discount land. Using the developer

referenced throughout this essay as an example, one might gain permission to develop faster.

(Winterton, 2007b) The location of the school in the developers subdivision persuaded the

Morgan County Council to allow development in a phase a year ahead of the allotted time. This

is potentially a windfall for the developer. As past research has suggested that housing supply is

at least partially determined by the time it takes to sell (DiPasquale, 1999). Thus a potential

extension of this research would be to analyze whether houses or building lots are sold quicker

when a new school is announced. Another reason to discount land for a new school is that it

could increase the attractiveness of the subdivision, when asked why the developer discounted

the land for the school the developer stated, a school makes development more marketable

(Winterton, 2006b, p. 2B). It may be that this could be chalked up to a cost of doing business

and that new schools make it easier to sell a vacant lot or even a home. It could be that the

goodwill garnered from such a policy may lead to planning permission in other areas as well.


28/32


This strategy may not only make the subdivision more marketable but it may also make the

developer more marketable to local planning authorities. Consequently an appropriate extension

of this paper would be to measure the success of altruistic strategies in garnering more

development permission. One must consider the timeline in which this analysis occurred; this

was time of immense growth in house prices and dramatic falls. Given that Cellini et al (2010)

found that following the passage of school facility investment bond house prices raised

gradually over the two or three years following the election and persists for at least a decade

and the limitations from widening the time window in a difference in differences analysis. It is

within reason to suggest that it may be that the full weight of the value of the school was not

capitalized until later. Thus another potential extension of this research would be to analyze the

changing value of the new school on house prices over time, this may prove difficult due to the

limited data and the aforementioned drastic fall in house prices.

Could the strategy of discounting land for a public school lead the developer to do well by

doing good? Although it cannot be said with the analysis put forth above that they did not

profit by their actions, it is reasonable to assume that there may be opportunities for such a

strategy to be profitable.


29/32


References

Bayer, P., Ferreira, F., McMillan, R. (2007). A unified framework for measuring preferences forschools and neighborhoods.Journal of Political Economy, 115, 588-638.

Benjamin, J.D., Boyle, G.W., Sirmans, C. F. (1992). Price discrimination in shopping centerleases. Journal of Urban Economics, 32, 299317.

Black, S. E. (1999). Do better schools matter? Parental valuation of elementary education.Quarterly Journal of Economics, 114, 577-599.

Bogart, W.T., Cromwell, B. A., (2000). How much is a neighborhood school worth? Journal ofUrban Economics,47, 280305.

Brueckner, J.J., (1993). Inter-store externalities and space allocation in shopping centers.Journalof Real Estate Finance and Economic, 7, 517.

Cellini, S.R., Ferreira, F., Rothstein, J.M., (2010). The value of school facility investments:evidence from a dynamic regression discontinuity design. Quarterly Journal ofEconomics, 125, 215261.

Chaudry-Shah, A. (1988). Capitalization and the theory of local public finance: an interpretiveessay.Journal of Economic Surveys, 2, 209-243.

Chen, V.W., Zeiser, K. (2008). Implementing propensity score matching causal analysis withStata [Power point] . Retrieved from http://help.pop.psu.edu/help-by-statistical-

method/propensity-matching/Intro%20to%20P-score_Sp08.pdf

Cheshire, P., Sheppard, S. (2004). Capitalising the value of free schools: the impact of supplycharacteristics and uncertainty.Economic Journal,114, 397424.

Chiodo, A.J., Hernandez-Murillo, R., Owyang, M. T., (2010). Nonlinear effects of school qualityon house prices. FederalReserve Bank of St. Louis Review,92, 185204.

Clapp, J.M., Nanda, A., Ross, S.L., (2008). Which school attributes matter? The influence ofschool district performance and demographic composition on property values.Journal ofUrban Economics, 63, 451466.

Daily News Staff Writer (2009, November 2). Ford Motor Co. gets it into gear, auto makerreports $1 billion 3Q profit.Daily News. Retrieved fromhttp://articles.nydailynews.com/2009-11-02/news/17940055_1_ford-escape-market-share-ford-focus

Dhar, P., Ross, S. L., (2010). School quality and property values: re-examining the boundaryapproach. Working paper. Department of Economics, University of Connecticut.


30/32


Dipasquale, D. (1999). Why dont we know more about housing supply? The Journal of RealEstate Finance and Economics, 18, 9-23

Gibbons, S., Machin, S., Silva, O. (2009). Valuing school quality using boundary discontinuities.Working paper. Spatial Economics Research Centre.

Hilber, C.A.L., Mayer, C.J., (2009). Why do households without children support local publicschools? Linking house price capitalization to school spending.Journal of UrbanEconomics, 65,7490.

Kane, T.J., Riegg, S.K., Staiger, D.O., (2006). School quality, neighborhoods, and housingprices.American Law and Economics Review,8, 183212.

National Center for Education Statistics. (2009). Table 10.Total expenditures for public

elementary and secondary education and other related programs, by type of expenditureand state or jurisdiction: Fiscal Year 2009 [Data File]. Retrieved fromhttp://nces.ed.gov/pubs2011/expenditures/tables/table_10.asp

Parmeter, C.F., Pope, J.C. (2009). Quasi-experiments and hedonic property value methods. In A.List & B. Price (Eds). Prepared for the Handbook on Experimental Economics and theEnvironment (pp. 1-73). Edward Elgar Publishers.

Nguyen-Hoang, P., Yinger, J. (2011). The capitalization of school quality into house values: Areview.Journal of Housing Economics, 20, 30-48.

Oates, W.E. (1969). The effects of property taxes and local public spending on property values:An empirical study of tax capitalization and the Tiebout hypothesis. TheJournal ofPolitical Economy, 77, 957-971.

Pashigian, B.P., Gould, E.D. (1998). Internalizing externalities: the price of space in shoppingmalls. Journal of Law and Economics, 41, 115-142

Ries, J., Somerville, T. (2010). School quality and residential values: evidence from Vancouverzoning.Reviewof Economics and Statistics,92, 928944.

Rosenbaum, P.R., Rubin, D.R. (1983). The central role of the Propensity score in observationalstudies for causal effects.Biometrika, 70, 41-55

Thorsnes, P. (2000). Internalizing neighborhood externalities: the effect of subdivision size andzoning on residential lot prices.Journal of Urban Economics, 48, 397-418

Tiebout, C.M. (1956). A pure theory of local expenditures. The Journal of Political Economy,64, 416-424.


31/32


United States Census Bureau. (2010a). Profile of General Population and HousingCharacteristics: 2010 [Data File]. Retrieved fromhttp://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_

10_DP_DPDP1&prodType=table

United States Census Bureau. (2010b). Selected Economic Characteristics: 2006-2010 AmericanCommunity Survey 5-Year Estimates [Data File]. Retrieved fromhttp://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_10_5YR_DP03&prodType=table

United States Census Bureau. (2010c). Selected Economic Characteristics: 2006-2010 AmericanCommunity Survey Selected Population Tables[Data File]. Retrieved fromhttp://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_10_SF4_DP03&prodType=table

Utah Governors Office of Planning and Budget. (2008). Population Projections by County andDistrict[Data File]. Retrieved from http://www.governor.utah.gov/dea/projections.html

Winterton, D. (2007a, July 1). Donations expand school construction. The Standard Examiner.Retrieved fromhttp://digital.olivesoftware.com/Repository/ml.asp?Ref=U1NFLzIwMDcvMDcvMDEjQXIwMTIwMQ%3D%3D&Mode=Gif&Locale=english-skin-custom

Winterton, D. (2006a, February 18). Morgan plans school bond for second elementary otherwiseyear-round track. The Standard Examiner. Retrieved from http://nl.newsbank.com/nl-

search/we/Archives?p_widesearch=yes&p_multi=OSXB|HTLL|EEOL|&p_product=SNUNP&p_theme=snunp&p_action=search&p_maxdocs=200&s_dispstring=allfields(morgan)%20AND%20date(2/18/2006%20to%202/18/2006)&p_field_date-0=YMD_date&p_params_date-0=date:B,E&p_text_date-0=2/18/2006%20to%202/18/2006)&p_field_advanced-0=&p_text_advanced-0=(%22morgan%22)&xcal_numdocs=20&p_perpage=10&p_sort=YMD_date:D&xcal_useweights=no

Winterton, D. (2005, November 15). School board considers third bond election in seven years.The Standard Examiner. Retrieved from http://nl.newsbank.com/nl-search/we/Archives?p_widesearch=yes&p_multi=OSXB|HTLL|EEOL|&p_product=SNUNP&p_theme=snunp&p_action=search&p_maxdocs=200&s_dispstring=allfields(third%20bond)%20AND%20date(11/15/2005%20to%2011/15/2005)&p_field_date-0=YMD_date&p_params_date-0=date:B,E&p_text_date-0=11/15/2005%20to%2011/15/2005)&p_field_advanced-0=&p_text_advanced-0=(%22third%20bond%22)&xcal_numdocs=20&p_perpage=10&p_sort=YMD_date:D&xcal_useweights=no


32/32


Winterton, D. (2006b, January 13). Morgan board chooses elementary school site. The StandardExaminer. Retrieved fromhttp://digital.olivesoftware.com/Repository/ml.asp?Ref=U1NFLzIwMDYvMDEvMTMjQXIwMTMwMw%3D%3D&Mode=Gif&Locale=english-skin-custom

Winterton, D. (2007b, February 27). Future school helps subdivision. The Standard Examiner.Retrieved fromhttp://digital.olivesoftware.com/Default/Scripting/ArticleWin.asp?From=Search&Key=SSE/2007/02/27/22/Ar02201.xml&CollName=SSE_APA3&DOCID=91390&PageLabelPrint=6D&skin=StandardExA&AppName=2&sPublication=SSE&sQuery=future%20school%20helps%20subdivision&sSorting=%2553%2563%256f%2572%2565%252c%2564%2565%2573%2563&sDateFrom=%2530%2531%252f%2530%2531%252f%2532%2530%2530%2537&sDateTo=%2530%2538%252f%2530%2536%252f%2532%2530%2531%2532&ViewMode=GIF

Zahirovic-Herbert, V., Turnbull, G.( 2009). Public school reform, expectations, andcapitalization: what signals quality to homebuyers? Southern Economic Journal,75,10941113.

internalizing a good externality

Documents