Zestimate- A Property Valuation Tool

Zestimate (Zillow’s estimate) is a calculated value that represents an estimated market price for a home. This estimate is generated using a variety of data points, including:

• Public Data: Information from tax records and public property records.
• User Data: Inputs from homeowners and real estate agents.
• Recent Sales: Prices of similar properties that have been sold recently.
• Market Trends: Current real estate market conditions and trends.

For more information on how the property valuation is done visit Zillow

Zillow uses algorithms and machine learning models to process this data and produce an estimate of what a property might be worth if it were to be sold in the current market. The Zestimate is meant to give homeowners, buyers, and real estate professionals a starting point for evaluating property prices, though it is not an official appraisal.

Efficient Data Structures for Zestimate

To estimate a property’s price, the ML model looks at factors like location, price, and other details. With data on over 100 million houses, it’s important to quickly get information for a specific location to estimate prices accurately. Searching through all this data one by one would take too much time and resources. So, using efficient data structures and algorithms is necessary to make the process faster and more efficient.

Quad-trees

For property locations, imagine dividing a city into four regions. Each region is then subdivided into four smaller regions, and so on. This helps quickly find properties in a specific area. For example, if you want to find houses in the northeast part of the city, you only search within that specific quadrant and its subdivisions, making the search faster and more efficient.

B-trees

For property attributes like price, imagine a sorted list where each node can have multiple keys and children. This allows quick searches, insertions, and deletions. For instance, if you want to find houses priced between $200,000 and $300,000, the B-tree helps you quickly locate this range without scanning every property, speeding up the retrieval process.

Combining both Quad-trees for spatial data and B-trees for other attributes ensures efficient storage and quick access to the vast dataset required for Zillow’s Zestimate. This method optimizes both space and time complexity, enabling the machine learning model to process data swiftly and accurately.

Implementation

1. Create a Quad-tree to store property locations based on geographical coordinates.
2. Create a B-tree to index property attributes like price, number of bedrooms, etc.
3. Insert Properties: Add each property to the Quad-tree for location and the B-tree for attributes.
4. Query by Location: Use the Quad-tree to find properties in the desired area.
5. Filter by Attributes: Use the B-tree to narrow down the properties based on criteria like price range.
6. Combine Results: Retrieve the final list of properties matching both location and attribute criteria for the price estimate.

Time and Space Complexity for B Trees

• Time Complexity: O(logN)
• Space Complexity: O(N)

Time and Space Complexity for B Trees

• Time Complexity: O(logN)
• Space Complexity: O(klogN)

  Here is the full code implementation Code