2010 06-03 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
12/15/2017

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
22/15/2017
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
32/15/2017

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
42/15/2017

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
52/15/2017

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
62/15/2017
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).
2/15/2017

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
82/15/2017

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.
92/15/2017
Automated Valuation Models (AVMs) and Appraisal

102/15/2017
Statistical Modeling in Valuation
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.

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
square footage of enclosed porches, open porches, decks, basements, finished basements, and attached and detached
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), basements are roughly 1/3 finished.
Note: the data is currently insufficient in measuring square footage.
Model 1.
112/15/2017

Regression Results
The independent variables in Model 1 explain approximately 69% of the dependent variable-median sale price.
Model 1.
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
122/15/2017

Regression Results
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
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. An attached garage is worth $16 per square
foot, and a detached garage worth $7 need to recalculate the model to get better numbers.
132/15/2017
According to Rick, this would
be more like $15-$16 per
square foot.
Land value usually accounts for
~20% of total cost so the $66,000
is very high for a median price of
%135,000.

Regression Results
Model 2.
142/15/2017
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 82% of results.
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.
The Bar Chart in Figure 5 shows the neighborhoods listed according to the number of homes sold between 2001 and
2009. The Table included in Figure 5 shows the value added or subtracted from a given house based on neighborhood
location.

Regression Results
15
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
2/15/2017
Figure 5.
Number of Homes Sold by Neighborhood 2001-2009
Neighborhood
Value Added to
Sale Based on
Neighborhood
LocationsAlgonquin $50,180
Auburndale $33,662
Audubon $10,711
Avondale $14,581
Bashford Manor $28,058
Beechmont $34,952
Belknap $12,575
Bon Air $18,829
Bowman $11,511
Brownsboro $16,323
Camp Taylor $25,422
Cherokee Gardens $63,693
Cherokee Seneca $71,350
Chickasaw $51,233
Cloverleaf $28,574
Hazelwood $39,948
Highland-Douglas $62,944
Jacobs $45,009
Kenwood Hill $36,746
Klondike $20,104
Park DuValle $67,488
Poplar Level $17,973
Portland $50,710
Prestonia $33,477
Rock Creek $31,934
St Joseph $15,549
Shawnee $50,212
South Louisville $42,269
Southland Park $35,217
Southside $38,684
Taylor-Berry $45,528
Tyler Park $67,223
The Bar Chart (left) in
Figure 5 shows the
neighborhoods listed
according to the
number of homes sold
between 2001 and
2009. The Table (right)
shows the value added
or subtracted from a
given house based on
neighborhood location.
(Numbers in red
indicate negative
values.)
Given houses
with identical
characteristics,
the sale price
could be
roughly
$60,000 more
than the
median of
$135,000 in
Cherokee
Gardens, and
$50,000 less
than the
median in
neighborhoods
such as
Algonquin,
Chickasaw,
Portland or
Shawnee.

2/15/2017 16
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.

2010 06-03 pilot study 1950s with-basements

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