---

AMES Housing Price Modeling

Ryan McDonald

---

Problem Statement

Story County requires Property Tax Equalization to occur every 2 years. Ames, IA is the most populated community in the county. Tax assessment rates have not increased in nearly a dacade. We have been tasked to:

1. Develop opensource tools for County Assessors to utilize for home value assessments for Ames, IA

2. Provide insights on most value-added characteristics to aid potential homeowners when placing offers on Residential homes or selecting a sales price.

---

Executive Summary

Provided housing dataset was intensively reviewed, analyzed and adapted into a running predictor model. Several key findings were interpretted from the modeling:

• Housing prices, on average, decreased year over year from the data provided. This data was derived during the Housing Crisis and does not reflect current, or typical housing price changes (on average ~3% annual increase).

  • Basic linear regression models were produced using singluar features (overall quality and total finish square footage). These models produced much better results in comparisson to the 'null' model by nearly 8x.

  • More complex modeling with several transformations proved to provide even more reliable predictions accounting for nearly 90% of the variance in sales prices.

    Production model consists of 18 housing features (5 of which were engineered). These features were expanded by introducing polynomial-based interaction terms. Further scaling through StandardScalar transformation and bias introduction with LASSO Regularization fine-tuned the production model to produce excellent results!

• Overall quality of home features were determined to be the most inportant factor for determing accurate sale prices of homes. Interaction terms containing a 'quality' metric also proved valuable.

---

Data Description

Data utilized for the project analysis was obtained through the Ames, Iowa assessors office.

Dataset contains 82 columns which include 23 nominal, 23 ordinal, 14 discrete, and 20 continuous variables. Each variable represents a 'feature' of the home. There are 2930 seperate observations that range in sales data between 2006 and 2010:

Data Dictionary created for datasets utilized in this analysis (click to expand)

Column Name	Data Type	Description
Id	int64	Observation number
PID	int64	Parcel identification number
MS SubClass	int64	type of dwelling
MS Zoning	object	zoning classification
Lot Frontage	float64	Linear feet of street
Lot Area	int64	Lot size in square feet
Street	object	Type of road access
Alley	object	Type of alley access
Lot Shape	object	General shape of property
Land Contour	object	Flatness of the property
Utilities	object	Type of utilities available
Lot Config	object	Lot configuration
Land Slope	object	Slope of property
Neighborhood	object	locations within Ames
Condition 1	object	proximity to various condition
Condition 2	object	Proximity to various conditions
Bldg Type	object	Type of dwelling
House Style	object	Style of dwelling
Overall Qual	int64	Rates the overall material and finish
Overall Cond	int64	Rates the overall condition
Year Built	int64	Original construction date
Year Remod/Add	int64	Remodel date
Roof Style	object	Type of roof
Roof Matl	object	Roof material
Exterior 1st	object	Exterior covering on house
Exterior 2nd	object	Exterior covering on house
Mas Vnr Type	object	Masonry veneer type
Mas Vnr Area	float64	Masonry veneer area in square feet
Exter Qual	object	quality of the material on the exterior
Exter Cond	object	present condition of the material on the exterior
Foundation	object	Type of foundation
Bsmt Qual	object	height of the basement
Bsmt Cond	object	condition of the basement
Bsmt Exposure	object	walkout or garden level walls
BsmtFin Type 1	object	Rating of basement finished area
BsmtFin SF 1	float64	type 1 finished square feet
BsmtFin Type 2	object	Rating of basement finished area
BsmtFin SF 2	float64	Type 2 finished square feet
Bsmt Unf SF	float64	Unfinished square feet of basement area
Total Bsmt SF	float64	Total square feet of basement area
Heating	object	Type of heating
Heating QC	object	Heating quality and condition
Central Air	object	Central air conditioning
Electrical	object	Electrical system
1st Flr SF	int64	First Floor square feet
2nd Flr SF	int64	Second floor square feet
Low Qual Fin SF	int64	Low quality finished square feet (all floors)
Gr Liv Area	int64	Above grade (ground) living area square feet
Bsmt Full Bath	float64	Basement full bathrooms
Bsmt Half Bath	float64	Basement half bathrooms
Full Bath	int64	Full bathrooms above grade
Half Bath	int64	Half baths above grade
Bedroom AbvGr	int64	Bedrooms above grade
Kitchen AbvGr	int64	Kitchens above grade
Kitchen Qual	object	Kitchen quality
TotRms AbvGrd	int64	Total rooms above grade
Functional	object	Home functionality
Fireplaces	int64	Number of fireplaces
Fireplace Qu	object	Fireplace quality
Garage Type	object	Garage location
Garage Yr Blt	float64	Year garage was built
Garage Finish	object	Interior finish of the garage
Garage Cars	float64	Size of garage in car capacity
Garage Area	float64	Size of garage in square feet
Garage Qual	object	Garage quality
Garage Cond	object	Garage condition
Paved Drive	object	Paved driveway
Wood Deck SF	int64	Wood deck area in square feet
Open Porch SF	int64	Open porch area in square feet
Enclosed Porch	int64	Enclosed porch area in square feet
3Ssn Porch	int64	Three season porch area in square feet
Screen Porch	int64	Screen porch area in square feet
Pool Area	int64	Pool area in square feet
Pool QC	object	Pool quality
Fence	object	Fence quality
Misc Feature	object	Miscellaneous feature not covered in other cats
Misc Val	int64	Value (dollar) of miscellaneous feature
Mo Sold	int64	Month Sold (MM)
Yr Sold	int64	Year Sold (YYYY)
Sale Type	object	Type of sale
SalePrice	int64	Sale price (dollar)

The following databases were utilized in analysis:

Database Utilized	Features with Database
Pandas
Missingno	msno
Numpy
Seaborn
Matplotlib	pyplot
SKLearn - metrics	r2_score
SKLearn - linear_model	LinearRegression, Lasso, LassoCV, Ridge, RidgeCV
SKLearn - model_selection	train_test_split, cross_val_score
SKLearn - preprocessing	PolynomialFeatures, StandardScalar

---

Analysis

1. All utilized datasets were cleaned and modified to provide the needed information to complete the problem statement.
2. Additional, Engineered Features were added to better interpret 'object'-type data and to better take advantage of avaiable data features. Several of these engineered features were utilized in modeling and predictions.

• Preparred models grew in complxity and accuracy.

• Some models contained transformations and scaling to better manipulate the data to provide accurate results.

• Final results for Model #6a are below showing residuals and predicted sale price plotted against actual sale price.

• Table representing baseline statistics of Model #6a below:

3. Additional supporting analysis is provided in the code notebook for review, as well as additional insights. Indepth and detailed processing and review are featured throughout code notebooking within markdown and code- formatted lines.

---

Conclusions and Recommendations

Based on an analysis of the data above, there are several conclusion that can be determined:

• Although accuracies may vary based on new test data, Model #6a will be submitted to the Story County Assessors Office for review. This model was able to represent variance in sale price nearly 90% of the time. Further modification to the model can be made in order to utilize in different cities as well.

• Homeowners looking to buy or sell a home in Ames, IA should focus efforts on overall quality of features in the home. This will net the greatest increase in value. Total finished square footage and garage size would be the next characteristics to work on. Location will be important as well. The only neighborhoods with mean sales price above $300k are North Ridge Heights and North Ridge.