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F e r n a n d e z , K i e l y , a n d W a r r e n | 1

MUPTE: An Analysis of its Impact on Development in Downtown Eugene

Jake Fernandez Evan Kiely Nick Warren

EC 418: Economic Analysis of Community Issues Under supervision of Bruce Blonigen

University of Oregon

Acknowledgements We thank Brooke Eastman and Ken Kato of the University of Oregon Geography Department for providing the maps in this study, as well as Tim Duy for his advice and counsel on the project.


Executive Summary

This paper analyzes the economic impacts of the City of Eugene’s Multi-‐Unit Property

Tax Exemption (MUPTE) program. The MUPTE is intended to spur high-‐density development in

the core of Eugene. High-‐density development helps fulfill the goals outlined in the ‘Envision

Eugene’ report. Envisions Eugene’s overall goal consists of accommodating 34,000 more

residents and 37,000 more jobs by the year 2032. We use multi-‐unit building permit data

between June 1998 and March 2013 to determine MUPTE’s impact. The study also accounts for

the effects of time, neighborhood-‐specific effects, distance to campus, distance to downtown,

and student population growth on multi-‐unit construction.

Between June 1998 and March 2013, Eugene reported the following construction

summary for multi-‐unit housing:

New Buildings Quantity Units(Dwellings)

Apartments 361 3877

Condominiums 11 60

Duplex 187 358

Row/Townhouse 81 83

Total 640 4378

In the 15 years our analysis covers, 16 of these projects (2.5% of the 640 total) have been

MUPTE approved projects, accounting for 732 dwellings (16.7% of the 4378 total) .

Key Findings


Maps created by the University of Oregon Geography Department aided us greatly by

providing a visual description of the pattern of multi-‐unit housing in Eugene (See Appendix A).

From these we see that there is a tendency for multi-‐unit housing development to cluster. The

biggest cluster is in and around the core areas of Eugene. Key inferences that we were able to

make from the maps include:

- There is substantial non-‐MUPTE development in MUPTE zones.

- There is a wide dispersion of multi-‐unit housing throughout Eugene.

- West University sees a disproportionate amount of new buildings compared to other

neighborhoods surrounding campus.

- Unlike other areas, nearly all Downtown development projects received the MUPTE.

Our regression analysis suggests the MUPTE has mixed effects in influencing

development in West University and Downtown. We find the MUPTE program adds 44

dwellings to West University during the years it is in existence. In Downtown, we find that

development is influenced by student growth and that the MUPTE is statistically insignificant.

However, if one excludes the Capstone project from the sample, there is no remaining

statistical evidence that anything spurs development in Downtown, including student growth.

We also find that student population is not a driving factor in the increase of multi-‐unit housing

anywhere in Eugene, except for the West University neighborhood.


Introduction

The City of Eugene’s Multi-‐Unit Property Tax Exemption Program (MUPTE) was created

to encourage economic development in Eugene while focusing on the downtown area in recent

years. The program started off as an economic incentive tool aimed at relieving the tax burden

for developers. With this program developers building, adding to, or converting to multi-‐unit

housing have the opportunity to apply for a tax exemption on the finalized project, at a

maximum of 100 percent exemption for 10 years.1 This paper analyzes the role MUPTE plays in

encouraging development in Eugene.

The motivation for this analysis stems from a number of sources. The city of Eugene is

interested to know if MUPTE is an efficient economic incentive to promote growth in the areas

it targets. Our approach examines the evidence that the availability of MUPTE in certain areas

of the city is associated with additional high-‐density urban growth in these MUPTE zones. We

also test the power of other possibly significant development factors, such as student

population. This analysis also looks at what type of developer is most likely to respond to

MUPTE incentives. Finally, we examine whether MUPTE diverts multi-‐unit development away

from the periphery and into the downtown core.

If MUPTE exemptions are not an efficient use of resources, then policy changes are

required for the existing program to better allocate the city’s limited resources. As of today, the

most influential driver behind the use of the MUPTE program is the developmental goals for the

city outlined in the ‘Envision Eugene’ report. This report was conducted by the city council in

collaboration with community leaders, business owners, and special interest groups. The

1 "Multi-Unit Property Tax Exemption."


document establishes a tangible set of attainable goals for economic development over the

course of the next two decades. Tentative steps outline Envisions Eugene’s overall goal of

accommodating 34,000 more residents and 37,000 more jobs by the year 2032. We aim to

provide guidance for future policy decisions, especially those regarding economic incentive

programs for development within the city of Eugene.

History

MUPTE began in 1977 with the explicit goal of “stimulating the construction of multi-‐

unit housing in the core area [of Eugene].”2 Developers apply for the tax exemption through an

application to the City of Eugene. The application must detail the building plans, demonstrate

financial need for the exemption, and meet a series of other requirements before approval is

given.3 Especially noteworthy requirements include demonstrating long-‐lasting public benefit,

requiring comment and approval from “the appropriate neighborhood association”4 and being

located within the downtown Eugene area.

These three requirements may pose significant barriers to developers interested in

using the MUPTE program. These problems comprise of monetary, political, and geographical

boundaries to adoption. Creating a plan that includes long-‐term public benefit generates

additional cost to each project as well. Allowing for the appropriate neighborhood association

to comment on public works means developers must listen to any grievances the local

community has with their project. This can and has resulted in lawsuits in the past. The

2 "Multi-Unit Property Tax Exemption." Eugene, OR Website. City of Eugene, n.d. Web. 18 Mar. 2013 3 Flannery, Amanda N. "City of Eugene Multiple Unit Property Tax Exemption (MUPTE) Application (For New Construction, Additions, or Conversions in the Downtown Area.)." City of Eugene. City of Eugene, Dec. 2011. Web. 18 Mar. 2013. 4 Ibid. Pg. 9


Capstone project created enough controversy that its developers agree to pay $260,000 to

settle one dispute.5 The last, limiting the location availability of MUPTE, is central to recent

plans of downtown re-‐development but may mean developers face a steeper cost due to higher

than market-‐efficient demand created by the MUPTE incentive.

The first exemption was given to the Broadway Center in 1978. Since then, more than

25 developments have used the program to earn tax exemptions. The smallest project to

receive an exemption had 7 one-‐bedroom units, while the second-‐largest had 170 units of

varying size. The most recent project to get an exemption, Capstone Student Housing on 13th

and Olive, was the largest and consists of 350 units of differing size available for rent.6 Over the

time period for this analysis, MUPTE projects have contributed a total of 732 units out of a total

4,378 that were built between June of 1998 and March of 2013.

The Capstone project created controversy over the goals and methods employed by the

MUPTE program to encourage downtown development. One issue was with the size of the

exemption, which represents approximately 8.5 million dollars in lost tax revenue.7 Another

issue was the concern that MUPTE would be used to encourage student housing. This poses a

potential conflict, as it is located next to a low-‐income senior citizen’s apartment complex.8

Both of these factors generated great public discussion and resistance to the program.

Following the Capstone project the city voted to temporarily suspend MUPTE on the 29th of

March 2013, with plans to reinstate it by July 1, 2013. It is the goal of the city to reinstate the

5 Adams, Tom. "Apartment Builder Settles Dispute with Neighbor." KVAL 13. KVAL.com, 4 Jan. 2013. Web. 06 June 2013. http://www.kval.com/politics/Apartment-builder-settles-dispute-with-neighbor-185711322.html 6 Christofferson, Lou. “MUPTE Approved Projects History.” City of Eugene Planning Division. Provided through direct correspondence. 7 McKee, Chris. "Eugene City Council Delays Vote on Student Housing Project's 10-year $8+ Million Property Tax Exemption Request." KMTR.com Breaking News. Newport Television LLC, 26 Apr. 2012. Web. 18 Mar. 2013. 8 Finnell, Shannon. "The Keys to Capstone." Eugeneweekly.com. Eugene Weekly, 2 May 2012. Web. 18 Mar. 2013.


program with new rules to address concerns about the necessity of MUPTE and the rules

governing how the city assigns exemptions.9

Timeline of MUPTE Program

Literature Review

The use of tax incentives is prevalent across US federal, state and local governments,

and there is an extensive literature discussing these programs. Because we are focusing on

Eugene’s Multi-‐family Unit Property Tax Exemption (MUPTE), we concentrated our literature

review on: why local government use tax incentives; the arguments for and against tax

incentives; and ways to replicate other research techniques. With respect to the empirical work

that analyzed the impacts of these programs, it is important to note that research on tax

programs often runs into data issues. Data is sometimes only published annually, outdated

9 McKee, Chris. "Eugene City Council Suspends Downtown Building Tax Break Program." - Breaking News, Local News, Local Weather, Local Sports. Newport Television LLC, 1 Mar. 2013. Web. 18 Mar. 2013.


before it is even published, biased, inaccurate, costly to acquire, inaccessible or completely

unavailable.10

Despite the existence of hundreds of publications on tax exemptions, there is very little

clear guidance for policy makers who want to generate economic development. The literature

does agree that taxes should matter for states, but does not agree on how, when or where.

Furthermore, firms may need tax incentives to locate to some areas, but research has yet to say

which businesses or which locations incentives actually work for.11 It is possible that many of

the tax incentives local governments’ use are not justified because they are unnecessary to

create economic development or even influence its location.

Noll and Zimbalast (1997) find three primary justifications for tax incentives.12 First, tax

incentives are justified if there are unemployed resources that could be used more productively

with incentives. Second, if a society is fully employed, it may be spending too little on

investment in relation to current consumption. By encouraging spending on investments with

tax incentives in the present, future consumption will increase. This future consumption will

increase at a greater level than the decrease in current consumption due to investments, which

then yields a net gain. Lastly, tax incentives are justified if the productivity created by them,

measured in terms of consumption, is higher than the productivity of all other feasible

investments. From this point of view tax incentives are thought of in relation to investments

10 Buss, T. F. "The Effect of State Tax Incentives on Economic Growth and Firm Location Decisions: An Overview of the Literature." Economic Development Quarterly15.1 (2001): 90-105. Print. 11 Ibid. 12 Noll, Roger G., and Andrew S. Zimbalist. Sports, Jobs, and Taxes: The Economic Impact of Sports Teams and Stadiums. Washington D.C.: Brookings Institution, 1997.


made by the government. These three justifications stem from the idea that tax incentives

should be used to address market failures that prevent the efficient use of resources.13,14

However, tax incentives may not be able to correctly address market failures. Wolman

(1988) finds that effective policy application may be hindered by imperfect information, as local

governments face uncertainty over a private business’s true intentions. Local governments may

then provide incentives to development that would have occurred anyway.15 In this case,

governments are foregoing revenue that they could be collecting.

Unfortunately, there are few research papers that focus on analyzing the success of city-‐

specific tax exemptions for urban redevelopment. Bondonio and Greenbaum (2006) analyze

local tax incentives and their effects in enterprise zones.16 They compared different states with

enterprise zones that use tax exemptions and other development tools to stimulate economic

growth. By comparing changes in measures like employment growth, productivity, and sales,

the authors found increases in most measures were offset by firms closing or leaving enterprise

zones. This study was not looking strictly at tax exemptions for development, but there are

many similarities with enterprise zones and Eugene’s MUPTE program.

Another relevant study was done by the Washington Department of Revenue (1996).17

Their research analyzed three tax incentives programs for distressed areas, one of which grants

exemptions on an individual basis. The application for this tax exemption requires firms to

13 Courant, P. (1994). How would you know a good economic development policy if you tripped over one? National Tax Journal, 47, 863-881. 14 Bartik, Timothy J. Who Benefits from State and Local Economic Development Policies? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research, 1991. 15 Wolman, H. (1988). Local economic development policy. Journal of Urban Affairs,10, 19-28. 16 Bondonio, D., and R. Greenbaum. "Do Local Tax Incentives Affect Economic Growth? What Mean Impacts Miss in the Analysis of Enterprise Zone Policies." Regional Science and Urban Economics 37.1 (2007): 121-36. 17 Washington Department of Revenue . (1996, September). Tax incentive programs.Olympia: Washington Department of Revenue, Research Division.


estimate the dollar amount that new investment would create. 60.7% of applications were

successful, and firms estimated that they would generate an additional $3.2 billion from

investment and the creation of 23,348 new jobs. The state deferred about $129 million in sales

taxes, as that is what they were granting exemptions for. The program existed for a decade and

only 5,997 new jobs were created, and $106 million was left outstanding on repayments for

deferred sales tax. Furthermore, distressed areas grew at a lesser rate than non-‐distressed ones

over the same time. It was found that declining companies took advantage of tax incentives in

distressed areas, whereas growing companies did not. Therefore, while the tax incentive made

distressed areas no better off, non-‐distressed area always improved. A paper done by Steven

Bourassa (1987) estimated an econometric model using housing permit data of Pittsburgh to

understand the effectiveness of tax incentives as a tool to influence new housing

development.18 Bourassa found that incentives increased the number of new units constructed.

The paper focused on understanding how city specific tax incentives could be used to create

housing development, and therefore is most like the problem we are facing.

Hypotheses & Supporting Evidence

This paper develops a statistical model of the location of new multi-‐unit housing in

Eugene since 1998 to examine the extent to which the MUPTE program has influenced these

location decisions. In this section, we hypothesize that a number of factors will affect location

18 Bourassa, Steven C. "Land Value Taxation and New Housing Development in Pittsburgh." Growth and Change 18.4 (1987): 44-56.


of multi-‐unit housing permit applications. The following section addresses visual evidence of

the locational outcomes of multi-‐unit housing in Eugene.

Hypotheses

Closely analyzing MUPTE reveals select questions this paper can address. Does MUPTE

encourage growth in MUPTE zones? Does MUPTE encourage new growth, or simply move

planned projects to MUPTE zones? Addressing these questions will provide an answer as to

whether MUPTE is an effective policy tool for generating development downtown. Our

approach examines these questions by analyzing the demand for multi-‐unit housing, especially

how it may affect the location of multi-‐unit construction.

The positive ideas expressed about MUPTE by developers and select city officials

suggest that it is an attractive tool for developers to use. Preliminary research using Eugene

government construction summary reports as of March 11th, 2013 shows that 640 new multi-‐

unit permits were issued since June 3rd, 1998.19 In that same time span, only 16 of all 640 new

projects were MUPTE approved. This means that MUPTE approved projects only account for

2.5% of all new multi-‐unit permits since 1998. We found this surprising, as the size of the tax

exemption that MUPTE grants should make it highly attractive to developers.

Hypothesis 1: MUPTE does not effectively influence developers to initiate projects in

neighborhoods that are in MUPTE zones. This hypothesis takes into consideration the relatively

small amount of MUPTE approved projects compared to total citywide construction. 19 "Eugene Construction Activity." Planning and Development Department. City of Eugene, 6 Mar. 2013. Web. 6 Mar. 2013.


Between the years 2000 and 2010, the population of Eugene grew 12.8%. This means

that approximately 19,000 new people chose to live within the Eugene city area.20 Within that

same time span, the University of Oregon student population grew by 31%, an increase of over

5,500 new students. In other words, in the last decade students have accounted for roughly

30% of population growth in the city of Eugene. A survey by Russell Kashian, entitled University

of Wisconsin-‐Whitewater Student Off-‐Campus Housing Survey, finds that students do have

locational preferences. Specifically, Kashian found “that off-‐campus students prefer to live at a

close proximity to campus.”21 In Kashian’s survey, the majority of respondents lived one or two

blocks away from campus. Given that he was analyzing the University of Wisconsin-‐Whitewater,

and not the University of Oregon, that level of specificity is likely unimportant. However, it does

speak to the general importance students place in proximity to campus.

Hypothesis 2: Student population growth at the University of Oregon is a primary driver

for multi-‐unit development in Eugene, especially in neighborhoods near campus and the

Downtown neighborhood.

Conversations with Hugh Pritchard, a local developer, led to the following revelation. It

is a common practice among developers to locate new housing projects within a one-‐mile

radius to a large university campus. This is due to student demand that allows for increased

rents base on proximity to campus. Because of the tax-‐exemption status of MUPTE zones, it is

likely housing development will be concentrated in these areas, to the detriment of other near-‐

campus, non-‐MUPTE zones. We have also observed a number of MUPTE applications

20 "Eugene's Population Growth." City of Eugene. City of Eugene Oregon. Web. 12 May 2013. 21 Kashian, Russel D., Ph.D. "University of Wisconsin-Whitewater Student Off-Campus Housing Survey." FISCAL AND ECONOMIC RESEARCH CENTER. University of Wisconsin-Whitewater, Department of Economics, June 2009. Web. 12 May 2013. 4.


immediately prior to the public suspension of MUPTE in March 2013. This suggests that

developers will apply for building permits sooner, to capture the tax exemption.

Hypothesis 3: The MUPTE will influence developers who already had intentions to build

in a general area to move the location into a MUPTE zone to benefit from it, known as

interlocational diversion. We also expect to find a similar phenomenon, intertemporal

diversion. Intertemportal diversion is where developers advance projects to capture MUPTE

before it is no longer offered in the area they are interested in developing, or delay

construction in a zone they have expectations will again have MUPTE status in the near future.

Downtown areas are typically more expensive to build and live in than nearby urban or

suburban areas. Because of the high cost, it is less attractive for developers to build multi-‐unit

housing in them. The MUPTE program is intended to help developers overcome some of the

costs associated with locating new projects in downtown areas. When taking into consideration

the amount of units associated with multi-‐unit housing, as opposed to total number of projects,

MUPTE approved development accounts for nearly 17% of the total units added since 1998.

This hypothesis is consistent with a conversation we had with local developer Hugh Prichard. In

our meeting, Mr. Prichard suggested that many of the smaller developers either don’t know

about the MUPTE or do not care to go through the paper work to apply for one.

Hypothesis 4: The MUPTE program will encourage larger projects to be located within

MUPTE zones, whereas smaller developers will not be influenced to change locational decisions

in order to gain access to the MUPTE incentive.

MUPTE Through Space & Time


To look at multi-‐unit construction activity and the presence of MUPTE incentives in

Eugene over our sample period, we had a series of maps created showing the location of all our

observations in the Eugene area.22 These maps, which divide Eugene into 19 neighborhoods,

revealed the preference of building location over the last 15 years. The first map, entitled All

Construction Projects, is an overview of the entire Eugene metro area. This map shows the

location of every multi-‐unit housing project since June 1998 through March 2013. In this map,

the density in terms of dwellings per project is differentiated by the size of the marker. These

markers are further identified based on whether or not they received the MUPTE, with green

dots representing non-‐MUPTE projects and purple dots indicating MUPTE approved ones. The

following six maps divide housing projects by their corresponding year. The year intervals are

based upon changes in the MUPTE boundary.

Construction Projects: July 1, 1998 – December 31, 2000 shows the years between 1998

and 2001 when MUPTE was in moratorium. This map serves as a control map and paints a

visual picture of where non-‐MUPTE eligible development naturally occurs in the Eugene

market. Comparing this map to subsequent years gives a comparison of how development

changes over time as the MUPTE boundary is shifted.

From these maps we can come to a few visual conclusions. First, it is apparent that

some non-‐MUPTE development occurs within MUPTE boundaries. This implies one of two

things. Either some developers do not need the MUPTE in order to build inside MUPTE zones,

or MUPTE actually encourages additional development within these MUPTE zones. After our

22 We thank Brooke Eastman, a GIS analyst with the Geography department at the University of Oregon, who constructed these maps of multi-‐unit construction in Eugene under the supervision of Ken Kato, Associate Director for the GIS program (See Appendix A)


conversation with developer Hugh Pritchard, it could also be the case that smaller developers

do not know about the program, or find the application process to be an unnecessary cost. The

average amount of dwellings per housing permit for all permits issued in our sample is 6.8

dwellings. The average amount of dwellings for MUPTE approve projects is 45.75, 23.88 if

Capstone is removed. This implies that there are definite size discrepancies between projects

that receive the MUPTE and ones that do not.

Secondly, the maps give visual evidence to the fact that there is a wide dispersion of

multi-‐unit development across Eugene. This suggests that the MUPTE program is having a

limited impact on concentrating development in the urban core. The dispersion of multi-‐unit

development occurs in distinct clusters throughout the city. One reason for this clustering

effect is that developers can take advantage of infrastructure already in place, thereby lowering

overall costs. This visual evidence is consistent with hypothesis 1 that the MUPTE does not

effectively influence developers to initiate projects in MUPTE zones. We define effective as

MUPTE’s ability to divert multi-‐unit housing from the periphery into MUPTE zones. Therefore,

the wide dispersion of multi-‐unit housing with the majority occurring outside MUPTE zones

gives evidence to MUPTE’s ineffectiveness.

Another take away from the maps is that the majority of multi-‐unit development that

occurs around the University is found in the West University neighborhood. This shows that the

demand for student housing is not evenly dispersed throughout the neighborhoods

surrounding campus. In the time this paper analyzed, multi-‐unit development consistently

occured in the West Unversity area and not in South University or other neighborhoods

surrounding campus. This provides contrary evidence to our hypothesis that the student


population would have a positive impact on multi-‐unit housing in neighborhoods surrounding

the campus. One possible reason for this is that West University is located closer to the

academic side of campus.

The final important note is that nearly all development that occurred downtown used

the MUPTE. This implies that MUPTE is crucial for downtown development. While MUPTE might

not be effective in influencing dense urban growth, it may be necessary for developers to be

able to build in the downtown neighborhood because of higher construction costs associated

with high density areas. This would mean that the projects built downtown would not have

been built without the presence of the MUPTE.

Model Specification

The base empirical model used for our analysis is as follows:

Dwellings i, j = βXi, j +εi, j

where Dwellings represents the amount of dwellings associated with each permit, at time i and

location, j . Xi, j , represents the explanatory variables used in our analysis.

Data & Variables

Finding the appropriate data to answer our three main questions was a limiting factor in

the scope of our analysis. The lack of readily available data narrowed the scope of which

questions surrounding the MUPTE program we would be able to answer. As is, the current


application for MUPTE does not require developers to provide estimates of the impact that

their proposed developments will have on the economy. Developers are also not required to

disclose financial information on the real costs and revenues they receive from their

development projects.

Duplicating the methodology used by Steven Bourassa (1987), we collected data on all

housing permits applied for since the creation of the MUPTE program, along with pre-‐MUPTE

housing data to act as a control23. This data was collected from the City of Eugene. For practical

reasons we were only able to acquire data on housing permits since June 1998. Associated with

each permit was information on the amount of dwellings for each project, its location and

neighborhood affiliation and year it was issued. We also incorporated information to whether a

project was in a MUPTE eligible zone or if it received the MUPTE. The following lists the

variables we used to generate our empirical model:

Variables

Housing Permits

To test our hypothesis, we will use the amount of dwellings associated with each permit

as our dependent variable. This allows us to answer questions relating to the scale of projects

that the MUPTE affects and avoids treating, for example, 100-‐unit projects to the same as 3-‐

unit projects. The variance in dwellings will show a more dynamic relationship than simply using

23 See Appendix C for a detailed description of how the data was collected on further reasoning behind the variables used.


permit applications, which tend to understate the impact of larger projects. For robustness, we

will also explore outcomes using projects as the dependent variable as a robustness check.

Measuring the Presence of MUPTE

MUPTE aims to encourage development in the area it targets, ideally allowing

developers to build who could otherwise not afford to do so. Previous studies and

conversations with former housing developers support the idea that the MUPTE plays a

significant role for multi-‐unit housing development.24 To measure the effect of MUPTE, we

create an indicator variable that takes the value of “1” when the MUPTE program is present in a

neighborhood of Eugene in a given year, and the value of “0” otherwise. We expect variables

controlling for the MUPTE to have a positive impact on dwellings.

Neighborhoods (Area)

There is strong evidence to suggest that distance to college campuses and rental

rates are inversely correlated25. In measuring distance-‐dependent variables there are multiple

avenues of measurement available. One option is to measure each permit’s distance from

significant locations within the city. A second way, the way analyzed in this paper, is to use the

24 Bourassa, Steven C. "Land Value Taxation and Housing Development: Effects of the Property Tax Reform in Three Types of Cities." JSTOR. The American Journal of Economics and Sociology, Jan. 1990. Web. 12 May 2013. 24 "Featured Tool: Multifamily Tax Exemption (MFTE)*." PSRC.org. Puget Sound Regional Council. Web. 13 May 2013. 24 Gray, Amy. "City of Seattle Multifamily Property Tax Exemption Program: 2011 Status Report to City Council." Seattle.gov. City of Seattle, 30 Mar. 2012. Web. 13 May 2013. 24 Benjamin, JD; Lusht, K M; Sinha, M. Search Costs and Apartments Rents. Workng Paper. January 1991. Web. 12 May 2013. 377. 24 Park, Do-Yeun. Determinants of Market Performance for Local Rental Housing Markets: Theory and Evidence. 25 Benjamin, JD; Lusht, K M; Sinha, M. Search Costs and Apartments Rents. Workng Paper. January 1991. Web. 12 May 2013. 377. 25 Park, Do-Yeun. Determinants of Market Performance for Local Rental Housing Markets: Theory and Evidence. 25 Kesseler, Tim. Rent Determinants for Student Housing: An Empirical Investigation of the Maastricht Area. 30.


neighborhood that a specific permit is located in and measure that neighborhood boundary’s

proximity to significant locations. Measuring the housing market using neighborhoods allows us

to control for any fixed effects that neighborhoods have on permit application. We expect to

find that neighborhood influences dwellings with regards to the neighborhood’s distance from

the University of Oregon.

Student Population

Past research suggests that students have locational preferences with regards to

housing and are willing to pay more to liver near the campus area 2627. Student growth

represents 30% of total population growth in Eugene and therefore may also impact housing

permits. A variable to control for the impact the student population may have on housing

permits is therefore included. We expect the student population to have a positive effect on

dwellings, especially in neighborhoods near the University of Oregon campus

Year

The year variable controls for any temporal effects that changes Eugene’s multi-‐unit

housing market on a year by year basis. It accounts for effects such as recessions, interest rate

fluctuations, and other factors that vary annually and have a common effect on multi-‐unit

construction across all locations in Eugene. A major potential impact measured by this variable

is “The Great Recession” that began in 2008. This recession led to “all racial and ethnic groups

26 Kashian, Russel D., Ph.D. "University of Wisconsin-Whitewater Student Off-Campus Housing Survey." FISCAL AND ECONOMIC RESEARCH CENTER. University of Wisconsin-Whitewater, Department of Economics, June 2009. Web. 12 May 2013. 4. 27 Sirmans, G Stacy; Benjamin, John D. Determinants of Market Rent. 364.


[experiencing] a decline in homeownership.”28 Fewer homeowners, even accounting for those

who lost homes and cannot afford to rent, leads to a greater number of renters.29 This is likely

to have positive impact on Eugene’s development of multi-‐unit housing, as many planned units

– such as the Capstone project -‐ are intended to be rental based and an influx of demand for

housing should lead to a greater supply.30 We expect year dummy variables to have a

significant impact on the amount of dwellings.

Developing the Empirical Model

Using OLS, the first regression only uses the MUPTE dummy variable as an independent

variable. By using the MUPTE dummy variable as the independent variable, we would be able to

observe if the presence of the tax exemption had any effect of the amount of multi-‐unit permits

applied for. Since the MUTPE was not in effect for the years between 1998 and 2002, we can

use these years as controls to capture any changes the MUPTE program caused when it was

reinstated. The MUPTE variable was significant, suggesting a relationship between the MUPTE

and multi-‐unit housing permits. This model does not take into account any other exogenous

variables that may have explanatory power on our model. For example, not all neighborhoods

in the city of Eugene have been eligible for the MUPTE, and the MUPTE boundaries have

changed as well. Additionally we were concerned with time variant aspects. These include, but

are not limited to, changing interest rates, fluctuating tax rates, and overall macro-‐economic

factors.

28 Ellen, IngrId G., and Samuel Dastrup. "Housing and the Great Recession." Stanford.edu. The Russell Sage Foundation and The Stanford Center on Poverty and Inequality, Oct. 2012. Web. 13 May 2013. 3. 29 DiPasquale, Denise. "Rental Housing: Current Market Conditions and the Role of Federal Policy." JSTOR. US Department of Housing and Urban Development, 2011. Web. 13 May 2013. 58. 30 "Capstone Collegiate Communities." Capstone-dev.com. The Capstone Companies. Web. 13 May 2013.


In order to address these concerns, variables for neighborhood association and years

were created in the mapping process, every observation was assigned a neighborhood that

corresponded with its address. Each neighborhood was assigned a number, 1 to 19 to

correspond to the 19 neighborhoods in Eugene (in alphabetical order). The two most important

areas for this analysis are area 6, which encompasses the Downtown neighborhood, and area

18, which is the West University neighborhood. Using this information, another data set was

created that organized the projects by area and year, rather than listing all individual projects.

The next step was to build Model 2, and to include dummy variables for both

neighborhood and specific years along with the generic MUPTE dummy. Having a specific area

dummy variable allows the regression to pinpoint growth by individual neighborhood as

opposed to the greater Eugene area. Including a year dummy in this model captures the specific

time variant aspects that are not specifically included that would influence growth. F-‐tests

conducted for each set of new variables indicated the year dummy variables were jointly

insignificant. All future models developed exclude year dummy variables for this reason. Area

dummy variables were kept due to joint significance. One possible limitation of this model was

that the MUPTE dummy variable did not capture the effects of the tax exemption on the two

specific neighborhoods that had the MUPTE eligibility, specifically the Downtown and the West

University neighborhoods.

To address this concern, we created interaction dummy variables that indicate the years

when the Downtown neighborhood had MUPTE presence and years when the West University

neighborhood had MUPTE status. These variables allow us to explicitly examine the impact of


MUPTE on the development of two key areas, Downtown and West University, which were the

primary two neighborhoods where the exemption was available for certain years of our sample.

Model 3 uses location specific MUPTE variables in replace of the generic MUPTE

dummy. Both the Downtown neighborhood and West University neighborhood dummy

variables came back significant, and did not change the significance of the previously included

area dummies. These two interaction variables take the place of the generic MUPTE dummy

variable in future analysis. The regressions until this point show support for MUPTE driving

growth in the Downtown neighborhood, as well as in the West University neighborhood.

However, these models do not take into consideration the impact of student population growth

from the University of Oregon on multi-‐unit development.

In order to address our hypothesis that student growth is a driver of multi-‐unit

development, additional variables were created to control for the student population. We

assumed that students would have a preference for living in neighborhoods around the

University of Oregon campus. This intuition is backed by the findings of Do-‐Yeun Park, as

mentioned earlier. The next step was to add interaction terms for student population over the

same years, and determine whether or not student population growth was a driving factor in

overall growth in the number of dwellings constructed. To do this, student population was

multiplied by areas 6, 18, 15 and 7. These four areas are the neighborhoods that correspond to

Downtown, West University, South University and Fairmont (East University), respectively.

Model 4 was created to test our second hypothesis, that student population is a driver for

multi-‐unit development surrounding the university. The inclusion of student population dummy

variables nullified the effect of the MUPTE in the Downtown neighborhood as the driving factor


for growth. The insignificance in the MUPTE area dummy for downtown is coupled with the

significance of the newly introduced student population area dummy for Downtown. However,

for the West University neighborhood both MUPTE and student population dummy variables

were statistically significant drivers of growth.

Our final concern in specifying these models is due to the scale of the Capstone project

on 13th and Olive. This project accounts for roughly half of the dwellings built with the MUPTE

tax exemption program in our sample. The development of these 350 dwellings created

controversy surrounding the MUPTE program, and has been a hot-‐button issue for the city of

Eugene and its surrounding community. Adding to the controversy is the fact that Capstone is a

strictly student housing project. For these reasons, we created a new dependent variable which

removed the 350 dwellings to the year Capstone’s permit was issued, this effectively controls

for the project. Model 5 explores the growth in dwellings without the Capstone project as the

dependent variable, and uses independent variables for MUPTE areas, neighborhood, and

student area dummy variables.

For robustness, the same procedure was completed using projects as the dependent

variable. As discussed earlier, this means that every permit is counted as one, removing the

scale of each project from the analysis.

Results

In the previous section we specified our base model for the effect the MUPTE has on

dwellings and further developed four supplementary models. Our first three models are shown

in Table 1. Model 2 introduces year and area dummy variables to capture time variant aspects


and area fixed effects not specifically included in our analysis. Model 3 uses dummy variables

for two areas, Downtown and West University, both of which have been eligible for the MUPTE

program over the given time frame. Models 4 and 5, shown in table two, use student

population variables to capture the potential effect student population growth has on multi-‐

unit development around the University of Oregon campus. Model 5 removes the Capstone

project from the data set to account for its size in comparison to all other MUPTE projects. An

in-‐depth analysis of these models is discussed next.

Table 1.

MODEL (1) (2) (3) VARIABLES dwellings dwellings dwellings MUPTE Dummy 45.46*** 50.73*** (9.764) (15.99) MUPTE * Downtown Dummy 42.75* (23.58) MUPTE * West University Dummy 60.12*** (20.42) Constant 10.99*** 32.82*** 34.06*** (2.504) (11.89) (10.21) Year Dummies No Yes No Area Dummies No Yes Yes Observations 304 304 304 R-‐squared 0.067 0.228 0.181 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1


In the base model, Model 1, the MUPTE dummy variable suggests that MUPTE

contributes 45 units a year for the years that the program was in place. However, this may be

due to spurious correlation from not controlling for other factors that affect demand for and

supply of multi-‐unit dwellings in Eugene. This would be an omitted variable bias.

To broadly control for possible omitted variable bias, Model 2 builds includes year and

area dummy variables to the base model. We found that after controlling for time variant

aspects and area fixed effects, the coefficient of the MUPTE dummy variable increases slightly

to approximately 51 units. Thus, we continue to have evidence for a MUPTE effect in this more

rigorous empirical specification. An F-‐test on the year dummies showed that the coefficients

on the year dummies are jointly insignificant. This indicates that random time variant aspects,

such as interest rates and other macroeconomic variables, do not significantly influence the

development of multi-‐unit housing over our sample period. An F-‐test was also conducted on

the area dummy variables and found them to be significant. Because of this we include area

variables as we further developed our empirical model but excluded year dummies.

Our third model helps to better analyze MUPTE’s effectiveness on the specific

neighborhoods that were targeted by MUPTE: The Downtown and West University areas.

Replacing our general MUPTE variable with MUPTE dummy variables specific to the Downtown

and West University neighborhoods, we find that the MUPTE had a significant impact on

development for both areas. The MUPTE dummy variable is found to be significant at the 1%

level for West University and significant at the 10% level for Downtown. The effect of the

MUPTE is more robust for the West University area, where the model suggests that 60 new

dwellings are added each year because of the MUPTE. In Downtown the MUPTE has less


significance and less of an effect, contributing approximately 43 dwellings. The inclusion of the

specific neighborhood MUPTE dummies did not take away the joint statistical significance of

the dummy variables for the other 17 neighborhoods in Eugene.

Table 2

MODEL (4) (5) VARIABLES Dwellings Dwellings no Cap capstone MUPTE Downtown Dummy -‐45.70 16.34 (33.75) (29.07) MUPTE West University Dummy 44.44** 44.44** (21.10) (18.18) Student Pop * Downtown 2.21*** -‐0.0688 (0.00617) (0.00532) Student Pop * West Univ. 1.01** 1.01*** (0.00446) (0.00384) Student Pop * South Univ. 0.133 0.133 (0.00421) (0.00363) Student Pop * Fairmont -‐0.114 -‐0.114 (0.00421) (0.00363) Constant 34.06*** 34.06*** (9.964) (8.584) Year Dummies No No Area Dummies Yes Yes Observations 304 304 R-‐squared 0.231 0.237 Standard errors in parentheses *** p<0.01, **p<0.05, * p<0.1

We next consider the effect of student growth on multi-‐unit construction in Eugene, as

its exclusion may also be a source of spurious correlation with the MUPTE variables. For

example, if MUPTE was established in only the neighborhoods experiencing strong student

demand, the MUPTE coefficients in the regressions above may be due to not controlling for this


increasing student population. Model 4 addresses these concerns by estimating the effect of

student population growth on the areas surrounding the UO campus in Eugene, as well as the

downtown area. When including these controls for the impact of increasing student growth,

the coefficient for the MUPTE Downtown variable becomes negative and insignificant. The

coefficient for the MUPTE interaction term with West University drops to 44 dwellings, but

remains significant at the 5 percent level. The new variable for the student population

interaction with Downtown is significant at the 1 percent level. This coefficient of 2.2 is in

percentages and indicates that, as student population increases by 100, this growth contributes

approximately 2 dwellings to the Downtown area. The significance of this variable, and the

insignificance of the Downtown MUPTE dummy, suggests that multi-‐unit development in

Downtown is driven by student population growth. The student population interaction term

with West University is significant at the 5 percent level and indicates that for every increase of

100 students, there is a 1.1 increase in dwellings in the West University neighborhood. The

model implies that the MUPTE and student population are both drivers for multi-‐unit housing

the West University area. The model implies that student population interaction terms for

South and East University (Fairmont) are both insignificant factors that do not influence multi-‐

unit housing development in those areas.

We also estimated Model 5, which has the same specification as Model 4, but removes

the 350 dwellings the Capstone project added in the year 2012. When we eliminate the

Capstone project from our sample, this negates the impact student growth has on multi-‐unit

development in the Downtown neighborhood because of the student-‐only nature of Capstone

units. After this change to the model, the Downtown MUPTE variable remains insignificant. The


change to the student population growth is drastic enough to suggest that the Capstone project

is an outlier in the data. Removing the Capstone Project also had the effect of raising the R-‐

squared of our model.

As a robustness check, we alternatively examined the effect of MUPTE on total number

of multi-‐unit projects, rather than total number of dwellings. The same process, as explained

earlier, was taken to create these additional 5 models. The results of this analysis are shown in

Appendix B. When using projects as the dependent variable the MUPTE was found to be

insignificant for all of the models. To remain consistent with the methodology, the same

process to account for the Capstone project was taken to exclude it from the final model. When

the Capstone project was removed, the only significant driver for multi-‐unit development,

other than the neighborhood specific variables, was the West University student population

interaction term. This was congruent with our intuition that dwellings would provide a more

dynamic explanation of the effectiveness of the MUPTE program.

Conclusion

The development and subsequent results of our models allows us to answer the

underlying questions of the MUPTE program and address our hypotheses.

Hypothesis 1: MUPTE does not effectively influence developers to initiate projects in

neighborhoods that are in MUPTE zones.

Results from Models 2, 3 and 4 show that the MUPTE has a significant impact on the

amount of dwellings associated with multi-‐unit permits in some MUPTE eligible neighborhoods,

particularly West University. This addresses our initial hypothesis. Due to the limitations of this

analysis, we are unable to definitively say whether or not the units that the MUPTE added were


strictly because of the tax exemption. However, since the MUPTE was shown to effectively

influence additional development in the West University, it may be the case that it has

influenced more development to locate there. In the Downtown neighborhood, it is possible

that the demand for multi-‐unit housing is so low and costs are so high that only those

developers who know their projects will be granted the MUPTE apply for the exemption. Model

5 further speaks to the level of effectiveness of the MUPTE program. This model shows that, by

accounting for the large-‐ scale Capstone project, any explanatory power that growth in student

population has on Downtown is nullified. What this means is that neither the MUPTE program,

nor the natural increase in student population, have any effect on multi-‐unit development

growth in the downtown neighborhood. In other words, if not for the Capstone project, there is

no statistical evidence that the MUPTE program has led to greater development of multi-‐unit

projects in the downtown area since 1998. Conversely, new multi-‐unit housing in the West

University neighborhood is driven both by growth in overall student population and the

existence of the MUPTE program. We can therefore not fully reject or accept our first

hypothesis. Instead, we can say that the MUPTE’s effectiveness is limited to the West University

area and there is no evidence to suggest that it influences developers to initiate new

development in Downtown.

Hypothesis 2: Student population growth at the University of Oregon is a primary driver

for multi-‐unit development in Eugene, especially in neighborhoods near campus and the

Downtown neighborhood.

From our analyses we can conclude that student population is not the primary driver for

multi-‐unit development in Eugene. Even when controlled for, the MUPTE still contributes 44


dwellings a year to the West University neighborhood. Student population also does not evenly

affect neighborhoods around the University of Oregon. When the Capstone project is taken into

consideration, student population only influences multi-‐unit development in West University

area. These findings partially reject our second hypothesis.

Hypothesis 3: The MUPTE will move developments towards MUPTE zones, creating

interlocational diversion from non-‐MUPTE zones, or intertemporal diversion from within the

same zone.

Due to the limitations of our methodology we cannot speak to what effect the MUPTE

has on interlocational diversion of multi-‐unit housing. This unfortunately limits the ability of our

analysis to speak to our third hypothesis. However, since some areas consistently have more

multi-‐unit development then others, despite not being in a MUPTE eligible neighborhood, we

can say that there is a diverse demand for multi-‐unit housing within Eugene. When years

dummy variables were included as an independent variable, we found them to be jointly

insignificant. This suggest that time variant aspects have no meaningful effect on multi-‐unit

development. It also provides some evidence that there is no intertemporal diversion, because

if there was, one would expect the years when MUPTE was instated to be statistically

significant.

Hypothesis 4: The MUPTE program will encourage larger projects to be located within

MUPTE zones, whereas smaller developers will not be influenced by MUPTE.

Limitations of our methodology prohibit us from addressing the fourth hypothesis put

forth in this analysis. However, the average size of MUPTE approved projects is 45.7 dwellings,

while the average size for all multi-‐unit projects is 6.8 dwellings. This leads us to intuitively


believe that MUPTE has been influential in encouraging large-‐scale projects within the MUPTE

boundary.

SUGGESTIONS & FURTHER COMMENTS

One of the most pervasive obstacles we faced was finding the appropriate data to

analyze. Because the MUPTE program does not require financial disclosure we cannot analyze

projects that have previously used the MUPTE to determine its effectiveness. Our methodology

is reliable for the data we have, but a more concrete way of determining the MUPTE’s

effectiveness would be a direct analysis of those projects which have been granted the

exemption in the past. Going forward, it is our recommendation that the city of Eugene amend

the MUPTE application process to require yearly financial reporting from developers for the

length of the tax exemption. Financial reporting would show year by year changes in revenue

and net income of the developers. This transparency would allow the city of Eugene to suspend

the tax exemption early if it turns out certain projects do not in fact need the MUPTE.

Data and methodology limitations prevented us from a robust analysis of any potential

intertemporal and interlocational diversion. A tax exemption program based on location is likely

to have some diversion, however, we cannot speak to what extent this diversion occurs.

Further analysis of MUPTE would do well to test for this movement. Specifically it is plausible

that the MUPTE in West University has influenced developers to move development from

South and East University area to gain access of the MUPTE. This would imply that the MUPTE

has been effective at concentrating high density development in the West University area.


From our analysis we were able to conclude that the MUPTE does indeed affect multi-‐

unit development in the West University neighborhood. However, when MUPTE is reinstated

we recommend staying consistent with the most recent boundary and excluding West

University from the MUPTE program. This is because of the significance of the student

population variable as a driving factor for growth in the West University neighborhood. We

believe that multi-‐unit development will occur in West University regardless of the existence of

the MUPTE program because of the attractiveness of this segment of the market.

From our analyses and conversations with developer Hugh Pritchard, it became clear

that the MUPTE program is not as widely known to developers as the city would like it to be.

We also noticed that the majority of smaller projects do not receive the MUPTE. These

discoveries lead us to believe that small-‐scale developers may be aware of the program. If the

goal is to promote multi-‐unit development in the Downtown area, then more emphasis should

be placed on educating the development community about the MUPTE program, especially the

smaller budget developers.


Appendix A: Maps of Eugene


Appendix B

Table 3. Project Regressions

(1) (2) (3)

VARIABLES Projects Projects Projects

Downtown MUPTE 0.500

(2.050)

University MUPTE

4.125**

(1.775)

a2 -4.500***

-4.500*** (1.244) (1.255)

a3 1.187 1.187

(1.244) (1.255)

a4 -4*** -4***

(1.244) (1.255)

a5 -4.375***

-4.375*** (1.244) (1.255)

a6 -6.555***

-4.562**

(1.622) (1.985)

a7 -4.250***

-4.250*** (1.244) (1.255)

a8 -3.250***

-3.250**

(1.244) (1.255)

a9 -3.562***

-3.562*** (1.244) (1.255)

a10 -1.250 -1.250

(1.244) (1.255)

a11 -4.750***

-4.750*** (1.244) (1.255)

a12 -2.875** -2.875**

(1.244) (1.255)

a13 -4.750***

-4.750*** (1.244) (1.255)

a14 1.750 1.750

(1.244) (1.255)

a15 -3.562***

-3.562*** (1.244) (1.255)

a16 -2.937** -2.938**

(1.244) (1.255)

a17 -3.625***

-3.625*** (1.244) (1.255)

a18 -0.579 -1.063


(1.424) (1.538)

a19 -3.687***

-3.688*** (1.244) (1.255)

MUPTE DUMMY 1.611* 3.157**

(0.933) (1.386)

Constant 1.989*** 4.236*** 4.812***

(0.239) (1.031) (0.888)

Observations 304 304 304

R-squared 0.010 0.325 0.280

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5. Project Regressions with and without Capstone

(1) (2) VARIABLES projects proj_wocap

mupte6 -1.009 -0.831

(2.978) (2.976)

mupte18 2.299 2.299

(1.862) (1.861)

a2 -4.500*** -4.500***

(1.243) (1.243)

a3 1.187 1.187

(1.243) (1.243)

a4 -4*** -4***

(1.243) (1.243)

a5 -4.375*** -4.375***

(1.243) (1.243)

a6 -11.30 -10.14

(9.930) (9.925)

a7 -1.952 -1.952

(7.850) (7.846)

a8 -3.250*** -3.250***

(1.243) (1.243)

a9 -3.563*** -3.563***

(1.243) (1.243)

a10 -1.250 -1.250

(1.243) (1.243)

a11 -4.750*** -4.750***

(1.243) (1.243)

a12 -2.875** -2.875**

(1.243) (1.243)


a13 -4.750*** -4.750***

(1.243) (1.243)

a14 1.750 1.750

(1.243) (1.243)

a15 -4.910 -4.910

(7.850) (7.846)

a16 -2.938** -2.938**

(1.243) (1.243)

a17 -3.625*** -3.625***

(1.243) (1.243)

a18 -24.60*** -24.60***

(8.047) (8.043)

a19 -3.688*** -3.688***

(1.243) (1.243)

student6 0.000377 0.000312

(0.000545) (0.000544)

student18 0.00117*** 0.00117***

(0.000393) (0.000393)

student15 6.46e-05 6.46e-05

(0.000371) (0.000371)

student7 -0.000110 -0.000110

(0.000371) (0.000371)

Constant 4.813*** 4.813***

(0.879) (0.879)

Observations 304 304

R-squared 0.304 0.304

Standard errors in

parentheses

*** p<0.01, ** p<0.05, *

p<0.1


Appendix C: Data collection and variable significance Data Collection and Methodology

The amount of the permit data we were able to collect was limited by the City of

Eugene’s database, which only contained digital permit data post June 3rd of 1998. Using their

online resources, we were able to collect a data set that contained all new construction

between 1998 and March of 2013. This data set includes information such as location of

individual projects, the permit type, who the developers were, application type, and proposed

use. The data was then filtered such that only residential permits were included. We further

filtered the data so that proposed use only included apartments, duplexes, condominiums, and

rows/townhouses, which allowed us to focus solely on multi-‐unit housing. When the filtering

was complete, we were left with a cleaner data set that was significantly smaller than what the

construction summary report had indicated.

After completing this initial cleaning of the data we were concerned that several MUPTE

projects of which we were aware did not appear in the data set. We then sought out staff from

the City of Eugene Planning and Development Department, who provided their own version of

multi-‐unit housing permit data. This data set contained slightly different project lists than what

we obtained from the online database. The next step was to combine and consolidate the two

data sets. Duplicates were deleted and in cases such as 90 Commons Ave (Stadium Park

Apartments), where several permits were issued for the same location at the same time with

varying unit sizes, the entries were consolidated into one observation and the units were

summed together. After this was completed, we had a data set of 639 observations, almost the

exact number that the most recent construction summary report provides.


For many of the observations, the units (dwellings) reported were inconsistent with

what one would expect to be associated with a multi-‐unit development. For example, many

projects were reported as having zero dwellings in them. In order to account for this, all

projects that were reported to have had zero dwellings in them were changed to contain 2

units, the minimum necessary to be considered a multi-‐unit project. Other projects were

described as having 4+ or 10+ units so they were changed to read as containing 4 and 10

dwellings, respectively. Observations that were reported of having 1-‐4 dwellings were set to

have 4. After doing this, we were left with a data set of 4,251 dwellings from 639 projects. Our

permit data set still does not match perfectly with what the Eugene construction summary data

would suggest, however it is remarkably close. We feel that the data set gathered is a fairly

accurate representation of the actual construction data and are confident in the information

we compiled.

From this data a spreadsheet was organized and each individual observation was

ordered by year. Information was then added about each project’s affiliation with the MUPTE

program. To show if a project was built in an area where the MUPTE was available it was given

a 1, and a 0 if it was outside the MUPTE boundary. This column became the “inside MUPTE”

dummy variable. The boundary for the MUPTE program has changed over the last 15 years. For

instance, the program was not available between the years of 1996 and 2001. Using the MUPTE

dummy variable allows us to capture the effects of a changing boundary zone. Next, another

dummy variable was created to show if the project received a MUPTE exemption or not. This

was used to map the locations of all multi-‐unit buildings in Eugene since 1998.


Significance of the MUPTE

Previous research by Steven Bourassa suggests that housing incentives do increase

development for larger cities. Analyzing three cities, he found that decreasing the tax rate on

land improvements in Pittsburgh had the effect of increasing the amount of new housing

construction in the urban areas.31 Bourassa found insignificant results for the other two cities

he analyzed, which were smaller that Pittsburgh. This suggests that land value taxation works

primarily for central cities, though Bourassa does qualify that notion by suggesting that data

problems and other types of property may work for different cities. Further analysis will be

conducted to determine if MUPTE is significant for Eugene. As Eugene likely does not qualify as

Bourassa’s ‘large city’, the author’s work suggests that it is unlikely that a program such as

MUPTE would have a significant effect in a town such as Eugene.

However, conversations with local developer Hugh Prichard, owner of Broadway Place,

provides anecdotal evidence that MUPTE does have an important effect on development in

Eugene. Broadway Place was MUPTE approved, and according to Prichard, would not have

been built without the tax exemption. Without the tax exemption, the rental rates that were

required to offset construction and loan costs would have been too high above current

comparable market rents. With MUPTE, Prichard faced fewer development costs and was able

to charge rents that fit within Eugene’s housing market. Broadway Place is an example of

MUPTE doing precisely what it was intended to do and gives support to the significance of the

MUPTE variable and its overall importance to local developers.

31 Bourassa, Steven C. "Land Value Taxation and Housing Development: Effects of the Property Tax Reform in Three Types of Cities." JSTOR. The American Journal of Economics and Sociology, Jan. 1990. Web. 12 May 2013.


Other cities run programs similar to MUPTE, also with the intention of attracting

residential development in urban centers. The widespread use of similar program offers reason

to consider MUPTE as a significant factor for urban development. Puget Sound runs a similar

program, and they conclude that it has “been effective in producing multifamily units in the

region’s larger cities.”32 Seattle also runs a tax exemption program and has found success it its

implementation.33 The MUPTE program may be ineffective if developers receive the MUPTE

when they do not financially need it in order to undertake their projects. As mentioned by Noll

and Zimbalist, tax exemptions are effective if they address inefficiencies in the market.

Therefore if the market does not exhibit any inefficiencies then it is unnecessary to use any tax

exemptions.

Distance to Campus

Benjamin, Lusht, and Sinha (1991), find that as walking time to campus increases, there

is a negative effect on rental rates.34 That is, rent and distance to campus are inversely

correlated, with higher rents found nearer campus. Do-‐Yeun Park, Determinants of Market

Performance for Local Rental Housing Markets: Theory and Evidence, also finds that demand for

housing rises as distance to campus decreases. This is reflected in rent prices, with higher rents

representing a greater demand for housing. Park finds that “the value of an apartment

increases by 24 percent for each mile that a property is closer to the university campus.”35

32 "Featured Tool: Multifamily Tax Exemption (MFTE)*." PSRC.org. Puget Sound Regional Council. Web. 13 May 2013. 33 Gray, Amy. "City of Seattle Multifamily Property Tax Exemption Program: 2011 Status Report to City Council." Seattle.gov. City of Seattle, 30 Mar. 2012. Web. 13 May 2013. 34 Benjamin, JD; Lusht, K M; Sinha, M. Search Costs and Apartments Rents. Workng Paper. January 1991. Web. 12 May 2013. 377. 35 Park, Do-Yeun. Determinants of Market Performance for Local Rental Housing Markets: Theory and Evidence.


Though the campus in question is Purdue University, Park’s paper gives reason to consider

distance to campus as an important explanatory variable for Eugene’s housing market as well.

Another study by Tim Kesseler echoes Park’s findings. Kesseler’s paper, Rent

Determinants for Student Housing, studies the rent determinants of the student housing market

in the Maastricht area of the Netherlands. In his report, Kesseler found that “rental price for

student housing is driven mainly by location, either measured by ZIP-‐code or by distance from

the [Central Student District].”36 Interesting to note is that measuring linear distance and the

distance of different ZIP-‐codes from campus yields similar results. The latter will be further

explored by the variable Neighborhoods (area) in this study.

First, the MUPTE permit area largely follows neighborhood boundaries. The application

requires developers to collect comments from the neighborhood association that a permit is

located within, and not neighbors within a certain distance. By following the city’s preference in

measuring the housing market by neighborhood boundaries, we can target our analysis to focus

on city zones that have already been shown to be key identifying characteristics for individual

housing units.

Each neighborhood is controlled by a different association, and these associations

individually contribute their own comments on MUPTE applications. Because responses to

MUPTE applications vary by neighborhood associations, there may be some fixed effects that

exist for MUPTE solely based on neighborhoods. For instance, developers may choose to not

apply for permits in one neighborhood if that neighborhood association is particularly opposed

to multi-‐unit housing. There may also be neighborhood-‐dependent fixed effects that exist

36 Kesseler, Tim. Rent Determinants for Student Housing: An Empirical Investigation of the Maastricht Area. 30.


independent of MUPTE responses, but would nonetheless influence a developer’s decision to

build. For these reasons, neighborhoods will be included in our analysis.

Student Population

Between the years 2000 and 2010, the population of Eugene grew 12.8%. This means

that approximately 19,000 new people chose to live within the Eugene city area.37 Within that

same time span, the University of Oregon student population grew by 31%, an increase of over

5,500 new students. In other words, in the last decade students have accounted for roughly

30% of population growth in the city of Eugene. If students have locational preferences for

housing, as prior research suggests, the fact that students represent a significant amount of

Eugene’s growth becomes an important variable to consider.

A survey by Russell Kashian, entitled University of Wisconsin-‐Whitewater Student Off-‐

Campus Housing Survey, finds that students do in fact have locational preferences. Specifically,

Kashian found “that off-‐campus students prefer to live at a close proximity to campus.”38 In

Kashian’s survey, the majority of respondents lived one or two blocks away from campus. Given

that he was analyzing the University of Wisconsin-‐Whitewater, and not the University of

Oregon, that level of specificity is likely unimportant. However, it does speak to the general

importance students place in proximity to campus.

Prave and Ord conducted research that found that students prefer to live near campus.

The authors collected results from Penn State University students which “indicate that a

37 "Eugene's Population Growth." City of Eugene. City of Eugene Oregon. Web. 12 May 2013. 38 Kashian, Russel D., Ph.D. "University of Wisconsin-Whitewater Student Off-Campus Housing Survey." FISCAL AND ECONOMIC RESEARCH CENTER. University of Wisconsin-Whitewater, Department of Economics, June 2009. Web. 12 May 2013. 4.


majority of students desire an apartment unit located within a twenty-‐minute walk to

campus.”39 Once more, the specific distance to campus that students prefer is likely irrelevant,

but the preference is important to consider.

Findings collected by Prave, Ord, and Kashian find that students have a locational

preference near campus centers. This preference is important to consider due to the significant

role students have played in Eugene’s growth. This paper will analyze student population to

determine if student locational preferences influence new multi-‐unit housing permit location.

Year

The year variable controls for any temporal effects that changes Eugene’s multi-‐unit

housing market. It accounts for effects such as recessions, interest rate fluctuations, and other

factors that vary annually and have a common effect on multi-‐unit construction across all

locations in Eugene. Because there have been such marked economic factors over the last

decade, we predict this to be an important variable for explaining Eugene’s housing market.

A major potential impact measured by this variable is “The Great Recession” that began

in 2008. This recession led to “all racial and ethnic groups [experiencing] a decline in

homeownership.”40 Fewer homeowners, even accounting for those who lost homes and cannot

afford to rent, leads to a greater number of renters.41 This is likely to have positive impact on

Eugene’s development of multi-‐unit housing, as many planned units – such as the Capstone

39 Sirmans, G Stacy; Benjamin, John D. Determinants of Market Rent. 364. 40 Ellen, IngrId G., and Samuel Dastrup. "Housing and the Great Recession." Stanford.edu. The Russell Sage Foundation and The Stanford Center on Poverty and Inequality, Oct. 2012. Web. 13 May 2013. 3. 41 DiPasquale, Denise. "Rental Housing: Current Market Conditions and the Role of Federal Policy." JSTOR. US Department of Housing and Urban Development, 2011. Web. 13 May 2013. 58.


project -‐ are intended to be rental based and an influx of demand for housing should lead to a

greater supply.42

The positive impact of the housing crash is likely compounded by other aspects of The

Great Recession, most notably the retraining of displaced workers. “Participation in retraining

was especially high among displaced workers whose earnings were above expected levels prior

to their job losses and were below expected levels just after their job losses.”43 An increase in

retraining programs likely increases the student population of the University of Oregon. The

recession factors, and others unmentioned here, may significantly influence Eugene’s housing

market. By controlling for annual effects, we capture these influences in our analysis and can

determine their role in Eugene’s housing market.

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