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.​
Read More

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.
Read More

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.
Read More

Data Sources​

  • Spatial data (GIS).​
  • Transactional data.
  • ​Amenities data.
  • Land Zone data.​​​
  • Road Infrastructure Data​​​
  • Location Data
  • Land Feature​​​
  • Quotations​​​

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.