2010 pilot study 1950s with basements

Pilot Study: Use of Regression to Identify, Quantify and Interpret
Property Values In Louisville, KY
Prepared for
Donna Hunt
Chief Deputy of the Jefferson County PVA
By
Margaret Maginnis
May 2010
An Analysis of Sales in 2000-2009 for
1950s Housing Stock in Jefferson County, Kentucky
108/18/2015

The Research Question:
1. What is the effect on sale price of 1950s housing stock in Louisville, KY when a finished basement is
added? Does it matter how much of the basement is finished? Is there a point of diminishing returns? What
is the effect of location in different neighborhoods?
Using a simple linear regression model, sale values of 1950s housing for the period 2001 through 2009 were
examined based on selected characteristics.
Pilot Study: Property Values of 1950s Housing stock
Introduction
The regression analysis identified lot size, number of stories, finished size, number of bathrooms, finished basement area,
garages and neighborhood location to be the most significant characteristics to impact a home’s sale price. We found that
having some portion of a basement finished certainly added value to the home, but that value diminished when the
basement was more than 3/4s finished. The most significant indicator of home value was often the neighborhood in which
the home was located, as the results show in the following write up.
Findings
208/18/2015
Source Data: PVA 3/25/2010
Limitations of the Data and Next Steps
Limitations of the analysis included insufficient data on housing characteristics such as square footage of porches, decks,
and garages. The lack of such detailed information prevents the model from being as robust and reliable it might be
otherwise. Next steps would be to obtain more detailed data from the REMF_CHAR database and rerun the rgressions. In
order to do this, we need to obtain a variable ‘dictionary’ of the codes used in the REMF_CHAR database. Using the
REMF_Master merged with the REMF_char data, we could rerun the regressions and compare predicted values to actual
sales values.

The initial query of parcels from the REMF Master consisted of all single-family houses - a database of
218,376 records with information on PVA assessments and sales, but no information on housing characteristics. These
records were then linked to valid sales for the period 2001 through 2009 with a resulting file of approximately 50,000
records containing detailed information on housing characteristics.
The parameters for the pilot study were 1950s-era single-family housing with full basements. The decision to
use 1950s housing was predicated on the fact that there is a large supply of homes from that era in Louisville and the
sample would be relatively homogeneous. In fact, Louisville Metro has 45,848 homes built during the 1950s. Of these,
approximately 49% (22,504) include some sort of basement, with an average size (basement) of 700 square feet. After
merging the data with valid sales, the number of homes built in the 1950s with full-sized basements comprised slightly over
4,200 records. Selecting for single-family residences with full basements, built in the 1950s, with valid sale transactions
that occurred between 2001-2009, the final study included 1,481 records.
Records were examined first in Excel, then frequencies, comparison of means and regression models were
run in SPSS. The following section highlights the exploratory phase of analysis.
Methodology
308/18/2015

Exploratory Results
Typical characteristics of 1950s housing stock include:
* quarter acre (or less) lot
* 1 to 1.5 stories
* 1 bathroom
* small front porch or stoop
* 1200 sq ft with full basement
* one-third of the basement area finished
* detached garage
408/18/2015

Pilot Analysis: Property Values of 1950s Housing stock
Exploratory Results
Figure 1. Location of homes included in the analysis.
Most of the 1950s housing used in this analysis was built directly inside the Watterson Expressway or just outside of it. In
the Southwest portion of the city, many homes were built in and just south of Shively. In the East End, 1950-era homes
were built inside the Watterson in the smaller incorporated cities of Kingsley and Wellington, St Mathews, Brownsboro
Village, Indian Hills, Rolling Fields, Beechwood and Woodlawn Park, and in the Louisville neighborhoods of Brownsboro-
Zorn, Clifton, Rock Creek, Gardiner Lane, Highlands-Douglas, Hawthorne, Belknap and Bowman. In addition, homes built in
the 1950s just east of the Watterson in St Regis Park, J-Town and Bon Air are included.
N = 1,481
508/18/2015

Exploratory Results
Figure 3.
Median Sale Price by Year; 1950s Houses with Full Basement.
Between 2001 and 2009, the median cost of a 1950s home rose approximately 17%.
$115,000
$119,950
$126,275
$127,500
$132,500
$135,100
$137,500
$130,070
$134,000
$100,000
$105,000
$110,000
$115,000
$120,000
$125,000
$130,000
$135,000
$140,000
2001 2002 2003 2004 2005 2006 2007 2008 2009
Median Sale Price by Year of Sale
608/18/2015
N=546
N=496
N=624
N=539
N=573
N=471
N=299
N=394
N=310
Average number of sales per year = 473
Total number of sales of 1950s housing with basements = 4,252

Exploratory Results
To look further at the data, we divided the
records into 3 categories according to the percent of
finished basement.
We then examined sale price by year based on those three
categories.
Sale Year
Homes with less
than 30% finished
basement
Homes
between 30%
and 75%
finished
basement
Homes with
more than 75%
finished
basements
2001 $117,000.00 $115,000.00 $117,500.00
2002 $118,750.00 $122,900.00 $119,000.00
2003 $127,250.00 $127,450.00 $116,000.00
2004 $123,000.00 $131,000.00 $135,200.00
2005 $130,000.00 $137,500.00 $139,000.00
2006 $132,000.00 $137,500.00 $139,830.00
2007 $134,200.00 $140,000.00 $143,750.00
2008 $123,900.00 $138,500.00 $126,320.00
2009 $129,950.00 $141,750.00 $132,400.00
Median Sale Price
Although sale prices started to fall by 2008, between 2001 and
2009, median home prices rose 23% for homes with 1/3 to 3/4
finished basement, and 13% for those with full-finished
basements (i.e., 75% or more finished).
7
Frequency Percent
1 794 53.6
2 534 36.1
3 153 10.3
Total 1481 100.0
Valid
Above Grade Price per Square Foot, 1950s Homes with Full
Finished Basements
Sale Year
Homes with less
than 30% finished
basement
Homes
between 30%
and 75%
finished
basement
Homes with
more than 75%
finished
basements
2001 $91.71 $95.17 $94.56
2002 $95.87 $102.13 $99.78
2003 $100.05 $102.82 $98.79
2004 $102.34 $106.99 $114.52
2005 $104.67 $112.24 $114.13
2006 $105.47 $111.44 $116.70
2007 $110.86 $113.70 $119.93
2008 $108.20 $115.36 $127.09
2009 $107.92 $112.32 $110.80
Average Sale Price Per Square Foot (Above Grade)
Homes with 1/3 to 3/4s of the basement finished did better in
the marketplace in 2001-2003, but this trend began to change in
2004 when finished basements fetched a higher price per square
foot (until 2009).
08/18/2015

Exploratory Results
Average Sale Price per Square Foot in 1950s Homes with Full Basements,
Varying by Percent of Basement Finished
Figure 4.
Average sale price per square foot in 1950s homes with full basements.
While sales
were higher for
houses with
75% or more of
the basement
finished, Sale
prices for 1950s
homes with a
third to three-
quarters
finished
basement rose
at a higher rate,
on average 3%
annually until
2008.
Homes with less
than 30% of the
basement
finished-the
majority of the
study sample-
rose steadily
until 2008 when
prices began to
fall.
$95
$100 $99
$115 $114
$117
$120
$127
$111
$95
$102
$103
$107
$112 $111
$114 $115 $112
$92
$96
$100 $102
$105 $105
$111 $108 $108
$60
$70
$80
$90
$100
$110
$120
$130
$140
2001 2002 2003 2004 2005 2006 2007 2008 2009
> 75% finishedbasement
30%-75% finishedbasement
< 30% finishedbasement
N = 1,481
808/18/2015

Statistical Modeling in Valuation
AVMs incorporating mass appraisal models use data based on geographical/neighborhood identity and finds appropriate
‘comparable’ properties. In the case of assessor’s models, often more than 20 independent variables are compared
against the selling price of a home. Regression is often run to derive values relative to a specific home. The process
usually includes all properties in the given area because the assessor’s job is to properly value all properties and distribute
the tax burden evenly.
Some AVMs on the other hand value properties one at a time, like a fee appraisor. The process may be progressive
wherein the valuation algorithm is data-driven and starts with the identification of the subject property. Or the process
can be retrospective, based on predetermined valuation equations (much like assessor’s models.)
Pros and Cons to both:
1) Prospective method is cumbersome and blind to problems, but is also dynamic and can be more current than the
retrospective method.
2) The retrospective method has the advantage that it can be verified up to a point. Outliers can be seen in advance
before the information is released to the public.
The following things need to be explained in detail in the appraisal report whenever AVM output is used:
1. number of sales
2. sales not used and reasons why
3. sample size(does the sample represent the whole population or market?
4. method used to derive value---regression, artificial intelligence, expert system, etc.
5. independent variables tested, used and not used in the model
6. area analyzed
7. statistics that measure model accuracy
8. outcome measures (independent/dependant values)
9. clear rationale of the model
10. any other information that may affect reliability of the model. ( I.e., source of sales data, source of property data,
description of editing process)
NOTE: It’s more important to have the simplest most straightforward model with only a small set of variables that can
reliably predict value.
908/18/2015
Automated Valuation Models (AVMs) and Appraisal

1008/18/2015
Location, size of home, number of bedrooms and bathrooms are the most important variables used to determine sale
value. Other primary variables may include year built, house style, subdivision, number of car spaces, lot size and
basement finished square footage. However, the particular set of primary variables will differ from market to
market.
Most buyers have an upper-limit price constraint, AND a certain minimum level of amenities they prefer. Examples would
be a preference for quality, view, new kitchen and/or yard size.
Primary Variables
Secondary Variables
Fireplaces, garage type, pool and air conditioning may be considered secondary variables important enough to the
homebuyer to include in a model. These variables have some market impact but are less often significant in a
regression model. They can be important though when there is little difference in variation among the primary
variables.
For example, if a neighborhood has homes all built within a 2- year period and all between 1400-1600 square feet of living
area , then 2 primary variables can be excluded from the model ---age and gross living area---and other factors such
as size of garage, fireplaces, or floor plan may be included.
Other Variables
A third set of variables such as location of the laundry area, guest closet, fencing, flooring, patio or deck may influence
some buyers or have small value relative to the overall decision to buy. Variables at this third level tend to be subjective
or calculated by another method: construction quality, physical condition or functional utility for example.

1108/18/2015
The modeling process should always be under the constraints of the appraisal process, not the other way around. The
analytical process should be placed within other appraisal processes not over them. This way, the statistical analysis
and modeling process can correctly become a tool and a marketable product for appraisers. For users, the appraisal
product is more precise, more robust and more comprehensive.
One rule of analysis is any phenomenon under scrutiny must be ‘typical’, meaning that one would expect to find that
people behave in a pattern that is roughly repeatable and quantifiable. If 100 people purchasing 100 different homes
in the same area purchased those homes for unique reasons, it would be impossible to quantify the major factors
contributing to the real estate value.
Mass appraisal approaches offer the ability to approximate a multiple bid process. (see p 44 in “A guide to Appraisal
Valuation Modeling”). The county assessor usually builds mass appraisal models with wide nets—many variables.
This is partly because the county assessor’s goal is to distribute the tax burden equitably. The problem with models
based on the methodologies used by assessors arises when properties are not valued equitably---for example, if one
neighborhood is undervalued in comparison to a similar neighborhood in the same jurisdiction. Owners of the
former neighborhood would pay less property tax than those in the latter neighborhood.
In contrast, appraisal models for individual properties must focus on variables most relevant to that property, therefore
only variables that significantly affect the market are included---that is, variables that approximate the market
mechanism determining real estate value in a specific market.
The best approach would combine human interface in market definition and variable selection with appraisal theory on
top of statistical methods.
Modeling and Appraisal

Regression Results
Following the exploratory analysis, two regression models were run. The first model included a
dependent variable of sale price, and independent variables of lot size, above-grade square footage of the home, and the
presence of enclosed porches, open porches, decks, and/or attached and detached 1- and 2-car garages.
The descriptive statistics in
Model 1 indicate that the
average sale price of a 1950s
home with full basement
over the years 2001 through
2009 was around
$135,000. Lot sizes are
almost ¼ acre (0.205), the
finished size above grade of
the home on average was
approximately 1,298 square
feet.
1208/18/2015
R Square
Change F Change df1 df2
Sig. F
Change
1 .808 .652 .650 26393.437 .652 275.826 10 1470 .000 1.531
Model Summaryb
Model
R R Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
Durbin-
Watson
Mean
Std.
Deviation N
consider_3 135283.41 44611.390 1481
land_siz_1 .205557 .0787794 1481
finsize 1297.94 343.270 1481
num2baths .30 .479 1481
num3baths 1.40 .562 1481
att1car .06 .238 1481
att2car .05 .215 1481
det2car .26 .438 1481
det1car .00 .000 1481
enclosdporch .08 .266 1481
openporch .25 .432 1481
deck .09 .282 1481
Descriptive Statistics
Model 1

Regression Results
The variables in Model
1 that are significant
and help to explain the
sale price of 1950s
homes include those
circled in column 1 at
right, with t-scores
greater than 1.96
1308/18/2015
Standardiz
ed
Coefficient
s
B Std. Error Beta
(Constant) -3105.351 3030.683 -1.025 .306
land_siz_1 89799.752 10161.408 .159 8.837 .000
finsize 75.327 2.685 .580 28.057 .000
num2baths 8558.955 1561.101 .092 5.483 .000
num3baths 13492.596 1483.860 .170 9.093 .000
att1car 2311.670 3008.007 .012 .769 .442
att2car 13087.060 3622.831 .063 3.612 .000
det2car 2060.475 1615.259 .020 1.276 .202
enclosdporch -738.237 2670.203 -.004 -.276 .782
openporch -2239.453 1602.111 -.022 -1.398 .162
deck 1008.074 2443.259 .006 .413 .680
Model
Unstandardized Coefficients
t Sig.
1
Model 1

Regression Results
1408/18/2015
Model 2 was run with the same dependent variable of sale
price, and the same independent variables, but with urban
neighborhoods added to the model as independent variables.
With neighborhoods added the model is a better fit for the data
and explains 79% of results.
R Square
Change F Change df1 df2
Sig. F
Change
.894 .799 .791 20398.864 .799 99.219 57 1423 .000 1.592
Model Summaryb
Model
R R Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics Durbin-
WatsonModel 2
Mean
Std.
Deviation N
consider_3 135283.41 44611.390 1481
land_siz_1 .205557 .0787794 1481
finsize 1297.94 343.270 1481
num2baths .30 .479 1481
num3baths 1.40 .562 1481
att1car .06 .238 1481
att2car .05 .215 1481
det2car .26 .438 1481
det1car .00 .000 1481
enclosdporch .08 .266 1481
openporch .25 .432 1481
deck .09 .282 1481
Descriptive Statistics

Regression Results
1508/18/2015
Those neighborhoods that significantly
impacted sale price include the following:
Algonquin, Auburndale, Audubon, Avondale,
Bashford Manor, Beechmont, Belknap, Bon
Air, Bowman, Brownsboro, Camp Taylor,
Cherokee Gardens, Cherokee Seneca,
Chickasaw, Cloverleaf, Hazelwood, Highland-
Douglas, Jacobs, Kenwood Hill, Klondike,
Park DuValle, Poplar Level, Portland,
Prestonia, Rock creek, St Joseph, Shawnee,
South Louisville, Southland Park, Southside,
Taylor-Berry and Tyler Park.
num2baths .479
num3baths .562
att1car .238
att2car .215
det2car .438
det1car .000
enclosdporch .266
openporch .432
deck .282
N1_ALGONGUIN .03674
N2_AUBURNDALE .06354
N3_AUDUBON .21083
N4_AVONDALE .28888
N5_BASHFORD .23522
N6_BEECHMONT .13137
N7_BELKNAP .14320
N8_BONAIR .37999
N9_BOWMAN .15613
N10_BROWNSBOR .12887
N11_CAMPTAYLOR .07774
N12_CHGARDENS .05192
N13_CHSENECA .02598
N14_CHICKASAW .10341
N15_CLIFTONHEIG .08968
N16_CLOVERLEAF .21228
N17_CRESCENTHIL .07774
N18_DEERPARK .05803
N19_GARDINERLA .14092
N20_GERMANTOW .05803
N21_HAWTHORNE .14320
N22_HAYFIELDDUN .08968
N23_HAZELWOOD .08192
N24_HILANDDOUG .09680
N25_HIKESPOINT .24510
N26_IROQUOIS .12101
N27_IROQUOISPAR .18582
N28_JACOBS .07332
N29_KENWOODHIL .17892
N30_KLONDIKE .26983
N31_MERRIWETHE .02598
N32_PARKDUVALL .03674
N33_POPLARLEVE .18067
N34_PORTLAND .03674
N35_PRESTONIA .10341
N36_REMAINDERCI .08589
N37_ROCKCREEK .16018
N38_SAINTJOSEPH .08589
N39_SCHNITZELBU .03674
N40_SHAWNEE .07332
N41_SOUTHLOUISV .03674
N42_SOUTHLANDP .12101
N43_SOUTHSIDE .10341
N44_TAYLORBERR .08192
N45_TYLERPARK .04498
N46_WYANDOTTE .07774

Neighborhood
Value Added to Sale
Based on
Neighborhood
Statiscal
Significance
Algonquin ($66,149.94) -4.400
Auburndale ($48,268.75) -5.122
Audubon ($27,039.55) -5.688
Avondale ($28,999.92) -6.551
Bashford Manor ($41,439.70) -8.966
Beechmont ($50,318.16) -8.788
Belknap ($2,000.51) -.368
Bon Air ($33,844.03) -7.865
Bowman ($286.11) -.053
Brownsboro $4,719.28 .793
Camp Taylor ($43,575.47) -5.445
Cherokee Gardens $59,442.41 5.371
Cherokee Seneca $68,079.00 3.216
Chickasaw ($65,220.18) -9.954
Clifton Heights ($27,168.59) -3.796
Cloverleaf ($43,784.27) -9.068
Crescent Hill ($8,853.17) -1.112
Deer Park ($12,972.11) -1.292
Gardiner Lane ($13,474.14) -2.438
Germantown ($25,508.95) -2.514
Hawthorne ($5,521.38) -1.015
Hayfield ($16,631.36) -2.294
Hazelwood ($58,071.34) -7.483
Highlands-Douglas $50,696.49 7.358
Hikes Point ($29,495.77) -6.343
Iroquois ($53,334.72) -8.915
Iroquois Park ($41,383.43) -8.274
Jacobs ($61,531.29) -7.382
Kenwood Hill ($51,625.35) -10.220
Klondike ($34,016.74) -7.499
Merriwether ($48,865.57) -2.345
Park DuValle ($84,682.05) -5.637
Poplar Level ($33,128.37) -6.638
Portland ($66,064.49) -4.390
Prestonia ($48,620.94) -7.440
Remainder of City ($38,082.08) -5.155
Rock Creek $19,665.12 3.716
Saint Joseph ($31,967.95) -4.329
Schnitzelberg ($21,206.71) -1.404
Shawnee ($65,017.71) -7.808
SouthLouisville ($60,125.89) -3.988
Southland Park ($52,110.55) -8.518
Southside ($54,525.69) -8.297
Taylor-Berry ($64,244.56) -8.364
Tyler Park $52,843.32 4.224
Wyandotte ($62,111.66) -7.801
Regression Results
16
1
1
2
2
2
2
2
3
4
5
5
6
8
8
9
9
9
10
10
11
11
12
12
14
16
16
16
22
22
25
26
30
31
31
37
39
49
50
53
69
70
87
95
117
136
259
CHSENECA
MERRIWETHER
ALGONGUIN
PARKDUVALLE
PORTLAND
SCHNITZELBURG
SOUTHLOUISVILLE
TYLERPARK
CHGARDENS
DEERPARK
GERMANTOWN
AUBURNDALE
JACOBS
SHAWNEE
CAMPTAYLOR
CRESCENTHILL
WYANDOTTE
HAZELWOOD
TAYLORBERRY
REMAINDERCITY
SAINTJOSEPH
CLIFTONHEIGHTS
HAYFIELDDUNDEE
HILANDDOUGLAS
CHICKASAW
PRESTONIA
SOUTHSIDE
IROQUOIS
SOUTHLANDPARK
BROWNSBORO
BEECHMONT
GARDINERLANE
BELKNAP
HAWTHORNE
BOWMAN
ROCKCREEK
KENWOODHILL
POPLARLEVEL
IROQUOISPARK
AUDUBON
CLOVERLEAF
BASHFORD
HIKESPOINT
KLONDIKE
AVONDALE
BONAIR
N= 1,454
08/18/2015 Figure 5.
Number of Homes Sold by Neighborhood 2001-2009
The Bar Chart in Figure 5
shows the neighborhoods
listed according to the
number of homes sold
between 2001 and 2009.
Given houses with
similar
characteristics, the
sale price could be
roughly $68,000
more than the
median of $135,000
in the Cherokee
Seneca
neighborhood, and
$66,000 less in
neighborhoods such
as the Algonquin
Neighborhood.

Regression Results
The results of the regression in Model 1 suggest that the lot size contributes $66,500 per acre to the total
property value. The above grade finished size of the home is worth $56 per square foot, a half-bathroom
would be $6,000, with additional half baths at $1500-$2000. A full bathroom is worth $11,000, with
additional full baths roughly $3000-$4000. Basement area is worth $37 per square foot and finished
basements add $5-$10 per square foot to the overall cost
1708/18/2015
Land value usually
accounts for
approximately 20%
of total cost so the
$66,000 is very high
for a median price of
%135,000.
According to the 2003
Marshall & Swift Cost
Handbook, this was
approximately $15 per
square foot.
Model 1. Interpretation of the data:
Independent variables that influence market price. Market Value
Lot size $66,501
Number of stories $12,412
Finished size of home per square foot (above grade) $56
Half-Bathroom $6,373
Full Bathroom $11,037
Basement area per square foot $37
Finished basement area per square foot $5
Attached garage per square foot $16
Detached garage per square foot $7
SaleYear $3,334

08/18/2015 18
Conclusions
This study found that having some portion of the basement finished certainly added value
to the home, but that value diminished when the basement was more than 3/4s finished. The more
significant indicator of home value was neighborhood.
However, these regression models are flawed without the use of more refined variables
such as detailed housing characteristics weighted to the Louisville Market Area. Automated Mass
Appraisal Systems such as ProVal do not make this information available to property Valuation
researchers thus hampering the strength and value of our models.

2010 pilot study 1950s with basements

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