Based on the available dataset from Kaggle using R and Excel, a regression analysis was carried out and complemented by further real estate market analysis in order to identify a business opportunity that allows for the prediction of the optimum fair prices for real estate properties in the region of Ames, Iowa. The business opportunity based on this regression model focuses on the real estate asset management market, an industry consisting mainly of individuals and businesses, which invest, further develop and carry out real estate transactions.

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
20