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Introduction

The main purpose of this study is to provide an updated outlook on the housing market in Springfield and Clark County. This study also hopes to recognize neighborhoods that stand the risk of self-depreciation and provide subsequent policy recommendations that can guide the city in planning and utilizing its resources. For the purposes of the study, the county was broken into five (5) study areas: East, North, Rocking Horse and the County.

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DEMOGRAPHICS The changes and trends between 2000 and 2010 were analyzed for the following variables – Total Population, Median Family Income, Owner Occupied Household Units, Renter Occupied Household Units, Household Value, Vacancy Rates, Median Age. Total Population U.S. Census Data was used to gather this data.

Study Area 2000 2010 %change

East 15624 14843 -4.99872

North 23292 20548 -11.7809

Rocking 22185 20670 -6.82894

Southwest 8747 8609 -1.57768

County 74894 74328 -0.75573

Total 144742 138333 -4.42788

TRENDS:

0 10000 20000 30000 40000 50000 60000 70000 80000

east north rocking southwest county

Popu

latio

n

Study Areas

Total Population

2000

2010

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• Overall decrease in the population of Springfield and Clark County. • Unlike the previously, there is an across-the-board decline, primarily signifying

the County’s reaction to the economic downturn. • North has had the biggest decrease in population followed by rocking horse. In

the previous study rocking horse suffered the biggest loss in population. The variance in the North study group illustrates its sensitivity to the economy.

Households Units (Family and Non-Family) Households can be further broken down into household units that are occupied by the owners themselves or by renters instead. Household units can consist of a family living together or even non-family members living together in one house. Total Household Units

Study Areas 2000 2010 % change

East 6585 6175 -6.22627183

North 9478 8733 -7.860308082

Rocking 8456 7514 -11.14001892

Southwest 3282 3370 2.681291895

County 28847 29452 2.097271813

Grand Total 56648 55244 -2.478463494

TRENDS:

• Total number of occupied housing has declined. • Rocking horse is seeing the largest decline in housing. • The number of households has declined by a smaller percent than the population. • Southwest and the County have a modest increase in the household units while all the

other three study areas experience a drastic decreases in their household units. • Occupied housing units declining at a faster rate than population, indicates a growing

household size. Owner Occupied Units

Study Areas 2000 2010 % change

east 4356 3657 -16.046832

north 6057 5276 -12.894172

rocking 3610 2998 -16.952909

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southwest 2205 1966 -10.839002

county 24273 24072 -0.8280806 TRENDS:

• Overall decrease in ownership of houses by 6.52% • Severe decline in household ownership in East study area. • Uniform decrease; all study areas are decreasing. • All the study areas that are considered to be within the city limits of

Springfield have a significant decrease in ownership. • County, although decreasing, is doing so marginally.

GENERAL DEMOGRAPHIC TRENDS BY STUDY AREA East –

• Amount of vacancies have more than doubled as well as the percentage of vacancies (from the total number of household units). This tells us occupied housing is decreasing while vacancies are increasing.

• High (enough) median household income and value. • Considering the two above bullet-points, stabilization is attainable in this study area.

North – • Comparatively, North is doing fine; relative to its own 2000 statistics, it is doing poorly. • Doing poorly especially in the following areas

o Population- Highest percent of decrease o Household occupancy- decrease in the total number of households.

Rocking Horse – • Comparatively, the Rocking Horse study area is doing the worse. • There is a decline in every category, with the exception of median house hold income.

This signifies that higher income families are moving into thsese neighborhoods; meaning that neighborhood stabilization program (round 1) has worked and is still working.

• 80% of the houses in the rocking horse are in the D range (D+,D, D-) • The decreased number of occupied housing can be accounted for in the demolition rate,

which has been the highest in eight years, and is projected to continue (as it should). •

Southwest • Smallest population. • Renters are increasing at a higher rate than buyers in this area. .

Springfield as A whole • Shrinking • Weak housing market because of unsustainable ownership rate

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• Declining population because of loss in employment (due to industrial disinvestment) • Loosing its people and growth to the neighboring towns and as David Rusk calls it, “Low

Elasticity City.”

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THE ARBITRAGE MODEL The word “arbitrage” generally defines how a buyer might take advantage of price differences on a commodity in different markets. Loosely applying this model to the housing market helps illustrates one way that filtering begins in neighborhoods. In this model, the house is the commodity; and the neighborhood represents the (housing) market. According to the Arbitrage Model, income is a good predictor of household value, and when homeowners find that their house is valued at less than what their income would predict, they often sell, and at a discounted price—thus the filtering. By running a best-fit regression model we aim to identify neighborhoods where houses are valued at less than what their income would predict, and therefore at risk of “filtering down”. The following tables identify all the block groups (within their study area) that stand a risk of filtering via the arbitrage model

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The following equation is obtained from running a best-fit regression model. This equation states that the median family income can be a strong predictor of your house value. Using this formula, we can find where the household values should to be, depending on their median family income.

MHHV = -11983 + 1.289 MFI

Where: MMHV= Median Household Value MFI = Median Family Income

An interesting—and reassuring—aspect of the outlined at-risk block groups is that they come in pairs. This signifies that it is not merely block goups that are at risk of filtering down (through the arbitrage model), but instead it is entire neighborhoods.

AREA   BG  LABEL   HH  VALUE   INCOME   PREDICTED  HH  VALUE  

east   15003   60980   62705   $83,482  

east   15002   60900   56646   $74,437  

east   13001   60680   56117   $73,647  

east   13005   56950   52644   $68,462  

north   6004   65940   63750   $85,042  

rocking   6003   70620   62019   $82,458  

southwest   11023   49940   67500   $90,641  

southwest   11022   58885   58594   $77,345  

southwest   11021   56990   55435   $72,629