What Is Data Science

Data Science is practice of organizing and analyzing of data in order to gain insights that can be helpful for human making decision. Data Science is a combination of multiple fields, including Machine learning, statistics, data analysis and scientific method used to extract meaningful information from noisy data. It gives hidden insights and trends about the market which helps users to take valid steps in future.​

  • With the help of emerging Data science and predictive analytics tools and technologies, we can quick and accurately organize and qualify data, recognize market dynamics and optimize business strategies in various sectors.
  • Data Science in Real Estate helps identify and manage risks, forecast customer behavior and increase engagement to offer more personalized solutions to clients.​
  • With the help of various Machine Learning models like multiple regression, Decision Tree, Time series models are capable to predict the current market value.
  • In real estate data science plays vital role to analysis the market patterns, recognize risk and study customer behavior.
Concept of Data Science
  • Artificial Intelligence and machine learning based algorithm in AVM automatically detect vital patterns and trends in the data.
  • Analysis based on real time market value of property.
  • Find correlation between the market value with other attributes which triggers the value of market.
  • Uses Statistics concepts, machine learning, and programming language such as Python or R.
What is data

Data is an information that has been collected from various sources in long period of time and translated into a form that is efficient for processing. Data refers to information that is stored as digital codes that can be executed by machines. It can be used for decision making once it is analyzed. In real estate business, we use historical land transactional data, land attributes data etc.​

Data we can divided into two:

Qualitative Data:

Qualitative data are those data which cannot be measured in the form of numbers. It is also known as categorical data. It can be subcategorized into two:

  • Nominal data: Nominal data are those data which are having names. It has no order so if you change the order of data then the meaning will remain same.
  • Ordinal data: Ordinal data are those data which are having order and there will be no continuity between adjacent categories.
Qualitative Data:

Quantitative data are those numbers which can be measured in numerical form. It is also referred to as numerical data. It can be subcategorized into two:

  • Continuous data: Continuous data are those data which are measured on an infinite scale.
  • Discrete data: Discrete data are those data which can be measure on finite scale.
Type of Data Used in Estater Meter
  • Categorical data: Basically, used to denote a unique ID for a particular parcel so that it can be distinguish and also can be used to denote yes or no in the data, or in binary language 1 or 0.
  • Continuous data: These are the numerical data which can goes to infinity for example price of land. Based on these relationships and identified pattern our team develop a statistical model and improve
  • Discrete data: These data show the count value of a variables or features. It doesn’t go beyond a particular value.
Data Sources
  • Spatial data (GIS).
  • Transactional data.
  • Amenities data.
  • Land Zone data.
  • Road Infrastructure
  • Data Location
  • Data Land Feature
  • Quotations
Data Science Lifecycle in Real Estate

Data is an information that has been collected from various sources in long period of time and translated into a form that is efficient for processing. Data refers to information that is stored as digital codes that can be executed by machines. It can be used for decision making once it is analyzed. In real estate business, we use historical land transactional data, land attributes data etc.

Data we can divided into two:

1. Understanding Business Problem:

Having Understanding of Business problem is the most crucial part in data science. According to nature of business, it varies. In real estate business, the business problem we encounter are:

  • Which villa/land property should client buy to maximize my return?
  • In which city/town are clients most likely to live?
  • What feature/ property characteristics included into particular location?
  • What is the optimum sale or rent price for the property to be chosen?
2. Data Acquisition:

Data science relies on identifying relevant data as the first step. And we gather the data from various sources and our large chunk of data is coming from our GIS (Geographic Information Systems) team.

3.Data Preparation:

The data what we get are in raw form may having duplicates values, outliers, in categorical form so, it becomes very crucial to clean and prepare the data for further analysis. Technique use in data preparation are:

  • Data cleaning
  • Outlier treatment
  • Creating Dummies variables for categorical variables
  • Data handling with Missing and Duplicated data
4.Exploratory Data Analysis:

This is the step where actual art comes into play. In EDA one can know the hidden insight about the data, and decides which variables are good for business problem and model preparation. In EDA we use descriptive statistics to find hidden information from data.

5.Visualization:

After EDA it is very important to view the data by visualization techniques, it helps to find out the trends in data and gives the overview about correlation between variables.

6.Model Preparation:

In this step after deciding which machine learning model to use according to data, we train the model on training data and test them to select the best performing model.

7. Model Evaluation and Deployment:

For selecting the best performing model, it is necessary to evaluate the models.so, in this step we check the significance of variables or features and also check its R square, residual standard error and based on this result we select the best performing model.

8.Prediction, Validation and Comparison:

After finalized the best performing model its required by using the best performing model to predict the price on the data which our model never seen and validate the result of the prediction by plotting the result on GIS.

Data Science Perspective for AVM
  • Gives initial insights about AVM performance.
  • Uses Data science tools to generate insights reports.
  • Using Visualization tools for finding trends.
  • Helps to make highly accurate forecasts as data technology progresses which able to make more profitable decisions
  • By considering property characteristics and demographics situations, helps to forecast property returns in selective neighborhood.
  • Uses statistical tools for getting basic information and making assumptions.
Application of Data Science in Real Estate
  • Reveal Insights of property market value.
  • Analysis property difference in one area.
  • Forecast where property markets are heading in the future.
  • Compare the property and market​
  • Forecast future value of market.​
  • Help investors in finding good deals.