The document presents a critical analysis of a regression model developed to predict fair real estate prices based on various housing features and market conditions, with a focus on Ames, Iowa. It emphasizes the importance of quality-related variables in determining housing prices and discusses market trends following the 2008 recession. Additionally, it outlines a business model aimed at providing real estate asset management services, targeting institutional clients such as Bloomberg for enhanced pricing capabilities.

1. REALPRICE
CRITICAL ANALYSIS
03/28/2019

2. 1.
REALPRICE
Price your real estate right
2

3. 3
Table of contents
→ Market Analysis
→ Regression Analysis
→ Excel
→ R
→ Final Model
→ Limitations
→ Business Model
→ Opportunity
→ Value Proposition
Iowa State University, Ames

4. 4
Executive Summary
→ Consider different house features to develop a model that predicts a fair price,
based on market and regression analysis
→ Create a transparent model to be implemented in the real estate asset
management market
→ Use this as a basis for comprehensive calculations and expand the offering to
other cities and regions across the US
Reiman Gardens, Ames

5. 2.
Market Analysis
United States vs. Ames, Iowa
5

6. Developments since recession
→ Relatively relaxed lending conditions
and rising employment levels have
resulted in further investment in real
estate sector.
→ Housing prices have seen an increase
over recent years (see graph)
The real estate market:
United States
6* Compared to 4.7% between 2014 and 2019
Outlook
→ With anticipated economic growth, Federal Reserve will be forced to raise interest
over the next years, which will increase borrowing costs that in turn influence
demand for homeownership.
→ Sets potential limits to industry growth over the next five years (expected to grow
a lower pace: 2.3% between 2019 and 2024*)

7. Developments in 2018 (Iowa)*
→ Increase in median sales price
to USD 161,000 (↑3.9%)
→ Decrease in number of homes
sold to 41,387 (↓0.9%)
→ On average, houses were 64
days on the market (↓16.9%)
→ Decrease in available inventory
of single-family detached
homes, however, offset by
townhouse-condo properties
The real estate market:
Ames, Iowa
Differentiating factors for Ames
→ Demand for housing remained
steady even after recession in
2008, due to stable job market
(especially through ISU)
→ Decrease in construction labor
force (left the area after the
recession)
→ Today: Lack of available
building space increases prices
7* Compared to 2017

8. 3.
Regression Model
Excel & R
8

9. Allocation of variables
→ 72 out of 80 variables were
grouped into 11 categories:
→ Location (7)
→ House construct (8)
→ Utilities (1)
→ House Style (6)
→ Date (2)
→ Exterior Detail (4)
→ Basement (11)
→ Floor Measurement (7)
→ Garage (8)
→ House Amenities (12)
→ House Features (6)
Regressions in Excel
→ Variables with high p-values nor
signs that did not make sense
(variables of scales with negative
sign) were deleted
→ Several regressions produced a
very low R² with less than 50%
(some were still kept due to low p-
values and correct signs of
coefficients).
→ Mixing the variables from
different categories in further
regressions resulted in a final
outcome of 26 variables.
9

10. 10
Considering this final set of
26 variables, further
regressions were run in “R”:
→ Primary regression
resulted in 0.80 R² but
contained further bad p-
values
→ Continuous reduction of
further variables down
to 16 final variables for
the regression model
Regressions in R

11. 11
Remaining 16 variables and
forecasting model
Close Downtown
OverallQual
YearRemodel
MasVnrArea
ExternalQuality
Exposure
BsmtType1
TotalBsmtSF
1stFlrSF
2ndFlrSF
BsmtFullBath
KitchenQualNum
TotRmsAbvGrd
Fireplaces
GarageArea
WoodDeckSF
House Price (y) = -137,424 + 4,186xCDowntown + 14,221xOverallQ + 3,576xYearremodel +
30xMasVnrArea + 14,986xExternalQuality + 6,876xExposure + 6,552xBsmt +
12xTBsmtSF + 45x1stFlSF + 39x2stFlSF + 7,674xBsmFullBath + 11,493xKitchenQualN+
2,024xTRoomsAbvG + 8,510xFireplaces + 34xGarageArea + 26xWoodDeckSF

12. 12
→ 0.81 R² (i.e. 81% of change in the price
can be explained by changes in the
variables)
→ All p-values are very low (i.e. there is
a statistically significant relationship
between these variables and the
price)
→ Residual plots showed no patterns
→ The highest coefficients are derived
for quality-related variables, showing
their importance as determining
factors for individuals that consider
buying a house.
→ Checking the available data for all
1,460 houses, the model missed the
price by about 2% on average with a
standard deviation of 21%.
Regression Analysis

13. → Two test approaches
→ 80% bottom vs. 20% top and 80% / 20%
randomly divided through “R” into train
and test
→ for all regressions:
R² = 0.79, low p-values
→ for all forecasts:
average differences of 0.2% and
standard deviation close to 19%
→ Both approaches delivered very close
results to determine whether the model
is overfitted.
→ It can thus be inferred that the model is
useful to be applied in the prediction of
house price.
13* Compared to 2017
Overfitting Test

14. → Number of bathrooms vs.
differentiation of bathroom types
→ Model does not consider exterior
amenities like pool or barbecue
place (not enough data)
→ Sample size of 1,460 is very small
14
Limitations

15. Example 1
→ Listed price: USD 185,000
→ Difference: -0.88%
15
Examples
Example 2
→ Listed price: USD 215,000
→ Difference: 0.49%

16. 3.
Business Model
Real Estate Asset Management
16

17. 17
→ Mainly targeted to real estate asset management market (mainly individuals and
businesses that invest in, develop or carry out transactions in real estate).
→ Recent increases in housing prices has driven a rising demand for real estate
services.
→ Low interest rates on the US market as well as company’s and consumers’ rising
confidence has created further growth in the residential and non-residential
markets.
Business opportunity

18. 18
Value proposition
→ Offer new feature for Bloomberg Terminal
→ help asset managers in pricing houses they
consider for investments
→ determine additional value created through
remodeling or expansion and its influence on a
property’s selling price.
→ Major client will be Bloomberg L.P., which owns
the data driven platform, providing 24-hours
financial information of pricing data, financials and
news, including similar pricing developments in
relation to the stock market.
→ A new equation of the model will be released
every week, since the real estate market faces
slower fluctuations than other markets.
→ Estimated Revenue: USD 500,000

19. 19
→ Real estate brokers currently rely mostly on average square feet and their personal
instinct on demand and supply
→ New feature will provide another screen to showcase historic data, graphs, percentage
changes, cities comparisons and the possibility to combine variables according to
individual needs within a specific time period
→ Real estate asset manager will gather necessary information to determine the optimum
selling price and receive a recommendation, based on individual factors.
→ Provide a valuable opportunity to the industry to create a common standard of pricing
evaluation and generate fair treatment on the market.
Changing the industry…

20. Thank you!
TEAM 6
Joao Victor Maciel
Lucy Hernandez
Monika Berber
Shresth Sethi
Ursula Jansen
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