House price data cleansing and segmentation tool.

Posted on Jul 29, 2018

Project background

Land Registry publishes data for each housing sale transaction that is registered in England & Wales. This data has been used extensively for many analysis, from price evolution in time to the assessment of price differences between areas. This dataset is publicly available under the government licence and dates back to 1995.

The main variables we find in each dataset are the location of the transaction (with full postcode, used for geolocate each property), the type of asset (Flat, Detached, Bungalow,... and Other, which was discarded) and the price. Unfortunately, there is no qualitative or quantitative information of each asset (i.e. an area would be very useful).

The main issue working with this dataset, particularly when working with any transactional dataset (Residential, Offices, ...) is that the distribution is usually skewed, with values of just few thousand pounds to many millions. For this reason, we have created a tool with two main objectives:

  • Filtering the original dataset based on the Median Absolute Deviation times a factor to discard outliers.
  • Further, filtering the dataset results to segment the market data so users can focus on a range of upper or lower percentile of the sample

Cleanse the data and discard outliers

Although it is a common practice, it would be wrong to cleanse the data by only applying a minimum and maximum value filtering the whole dataset, mainly because of the two reasons listed below:

  • The highly skewed distribution of the price range
  • The variability of prices within each group

To tackle the first issue, we decided to implement the MAD (Median Absolute Deviation) instead of the standard deviation to be less dependent on the variance of the data and apply a factor below and above the median to filter the data. So, a '2' factor will discard values 2 times above or below the Median therefore the higher the factor applied will result in more extreme values to be included in the final sample.

At the same time and to avoid geographical bias, we apply this methodology for each Borough. That is, we calculate the median and MAD for each borough and with the calculated median, we then apply the MAD factor filtering. In other words, a 1million pounds house in a central Borough won’t be considered an outlier but it might be discarded if the house is located in one of the outer Boroughs.

 

 

Market segmentation

Real Estate investors market their properties to very specific market segments, i.e. for the top end of the market. Having this capability to dynamically select which 'slice' of the market we are interested in is, therefore, a very useful tool when working with housing prices.

With the percentiles slider implemented, the user can easily select the segment of the market he/she is interested in. For example, to analyse the top end of the market would be as easy as to select for 0.8 to 1 in the slider.

Download the data

The primary objective of this tool is to implement specific criteria to filter and segment a dataset. Hence, the download capability has also been implemented. We can download either the full datasets once is cleansed according to user selection or a Borough summary for convenience.

 Next steps

This application should be extended to allow the following:

  • To implement the capability to upload your own CSV.
  • Mapping each of the data properties to help with data visualization.
  • Making the coding as generic as possible to apply the same methodology to other country datasets.
Access the hosted Shiny Application: https://natxomoreno.shinyapps.io/London_House_Prices_Stat_Explorer/

About Author

Nacho Moreno

Nacho joined NYCDSA bootcamp from his current position as a location Intelligence analyst in JLL. With 10 years of experience in GIS currently working towards a new set of skills within the geospatial data science field.
View all posts by Nacho Moreno >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI