Machine Learning Project by Group E * disclaimer: The professor later told us that there were some improvements or missing details that should have been added to the regression analysis. But, this was our initial deliverable.

1. Presented by:
Bruno Cervantes Quino
Bruno Gobbet Gianini
Federico J. Garcia Lopez
George Kofi Akanza
Kamal Nandan
Melody Ucros
Peder Viland
Identifying New Opportunities
REGRESSION REPORT 2017
To: Sales Director

2. Executive Summary ...….….…………….…………….………………..…………….……….…. 3
Recommendations …….……………………………………….………………………………...... 4
Targeted Campaigns…………………....………….……….…………….………..... 4
Internal Alert System ………………..…………………….…………….………...... 4
Agent Compensation ..…………….…….....……………….………………………. 4
Analyst Approach ………...………..….……………….………….…………………………..…... 5
Data Preparation ……………………………………………………………….……..... 5
Variable Selection ………………………………………………………………….…… 5
Model Creation …………………………………………………………….......……… 5
Model Validation ………………………………………………………….…….…..... 5
Annex …………………………….…….……………..……………………………………….……..…. 6
Table of Content
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3. Executive Summary
As a Real Estate Agency, identifying good opportunities to help clients buy or sell a home is a key
task of our value proposition. The team of analysts has created a model with which we can
estimate rental prices based on a number of factors and gain a competitive advantage with our
offers in the market.
To create this tool, we used a dataset from idealista.com. The dataset consisted of numerous
descriptive variables that could influence price but, based on their statistical significance, the
team has only kept the following variables and some of their interactions:
• Sq. Meters of the property
• Area where the property is located
• Bedrooms that the property has
• If it is an Outer Property
• If it is a Penthouse or a Duplex
Note that certain descriptors have more influence over others when estimating price. For
example, key observations regarding the models predictive ability is that:
• When a property is a penthouse, or a property which we know the sq.mt. in the Area
of Retiro, our models estimation is the most accurate.
• When we know the sq.mt of properties in the Areas of Salamanca, Tetuan,
Chamartín, Centro and Hortaleza, our estimations can also be highly trusted.
• Our model works best for properties with rental prices below 10,000 or when the
property is above 10,000 Sq.Mts.
Therefore, these are the three initiatives that could help the agency gain a competitive
advantage when compared to competitors:
• Create Targeted Marketing Campaigns for these specific areas informing tenants
about the potential of their property in regards to buying, selling or renting prices.
• Build an internal tool where properties that meet a certain sq.mt threshold and
marked at a price below area standards, are automatically added to the pipeline of
opportunities.
• Establish Quarterly Goals for the agents to meet in these areas, since we can
significantly improve our offense strategy when finding properties now.
In conclusion, we can expect these initiatives to improve the ROI of the agents time, the
proactivity with which we approach clients, and the offers that we can negotiate in the market.
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4. Recommendations
Targeted Campaigns
Branding ourselves as helpful and knowledgeable about the market can help us attract new
clients and grow our business. The best way to do this is through targeted Facebook Campaigns,
with an informative video of different neighborhoods, a walking tour, the average prices, and how
to maximize the value of properties. Another way to do this is through an email or mailbox
campaign, but it would be a bit harder to measure our efforts and allocate our resources.
Therefore, if the second option in preferred, the best strategy would be to partner with specific
building owners or local businesses in these different areas. The information from the model will
be used in the video but also once these new clients approach us, so that we can more accurately
assess the opportunity in hand.
Internal Alert System
Creating an internal tool to keep track of good opportunities is essential. This can be done by
using software that can be configured to automatically discover and crawl real estate websites.
The initial configuration usually allows you to set data parameters like city, state, zip code, selling
price, rent price, address, property size and characteristics. Based on these parameters, listings
can be saved to database with normalized fields for easier access and search capabilities. The
information from the model will be used to create those specific parameters per area, in order to
automatically issue an alert once a good opportunity is identified.
Agent Compensation
Real Estate is a people-business, and that means treating agents and clients at a superb level.
The information on rental prices that the model provides can be used to create a more
personalized compensation structure for finding those “anomalies” in the market, and closing
them. We would already be providing them the tool to identify some, but it is up to them to
bring that business to our agency. This compensation structure can be driven by quarterly goals
that agents should fulfill in different areas, and the recruitment of clients that might not be
listed online.
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5. Analyst Approach
Data Preparation
The original dataset had the following information: ID, Area, Address, Number, Zone, Rent,
Bedrooms, Sq. Mt. , Floor, Outer, Elevator, Penthouse, Cottage, Duplex, and Semi-Detached. We
removed the columns that were un-factorable, were too narrow for our analysis, or had too many
missing variables. The dataset wasn’t too big, so we used Excel to clean it manually. We then
imported the prepared dataset into R Studio and factored several fields to be able to work with
them.
Variable Selection
In order to choose which variables to keep, we used Stepwise Forward method in R, which simply
kept adding variables that were significant until it could no longer be improved. This was the
final output: Rent ~ Sq..Mt. + Area + Outer + Bedrooms + Penthouse + Duplex. This was giving
us an R2 of .76, which meant that only 76% of the variance in rental prices could be explained
by our model. Stepwise method doesn’t test for interactions though, so we manually tested
different interactions of the variables and kept the ones that improved the R2.
Model Creation
To compare the original model with the manually modified one, we used an Anova test. Because
the p-value resulted in < 5%, then the variables added did matter and the new model was
significantly better. This was the final model used:
Rent ~ Sq. Mt. + Area + Outer + Bedrooms + Penthouse + Duplex + Area: Sq. Mt.
Model Validation
To validate our model we used K-Fold Cross Validation, dividing our dataset 80/20 for training
and testing. When testing, our final model yielded an R2 that ranged from .83 to .87. We also
created some graphs to visually assess the models prediction abilities. One helped us see the
average price per area, and another one which values might be having an impact in the curve of
our model. Another important conclusion from one of the graphs was that our model works best
for properties with rental prices below 10,000 or when the property is above 10,000 Sq.Mts.
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6. Annex
Average Rent by Area:
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Estimating Rent:

7. Annex
Identifying Opportunities:
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Stepwise Output:

8. Annex
Final Model:
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