Chrome Extension: Smarter Size Estimation For London Rentals

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Hey everyone! Let's dive into how we can make our Chrome extension even better, specifically focusing on a crucial feature: size estimation for London rentals. We all know how important it is to get accurate square footage (sqft) information when you're hunting for a new place. It's often the first thing people look at. But what happens when the data isn't readily available? That's where our new approach comes in! This is all about creating a smart fallback system to ensure we always have a size estimate, even when the original data sources fail. This is crucial because sqft is the most important feature (size_bin = 0.081). Let's be real, a lot of listings on Rightmove, like about 28%, have the sqft data readily available in their JSON. We then have OCR which adds about 40% coverage. But what about the remaining 30%? We got you covered. This is the game plan: Generate median sqft by (postcode_district, bedrooms) from training data.

Why Accurate Size Estimation Matters

Accurate size estimation is key when searching for London rentals. This project directly addresses the significant gap in size data coverage, ensuring users have access to reliable sqft information across a wider range of listings. As we mentioned, sqft is the most important feature, and having a good estimation process will dramatically improve the user experience. Without a robust fallback, users are left in the dark, and that is not what we want. We're talking about a significant chunk of listings, and we need to make sure we've got a plan. So, the main question is, how do we make sure our Chrome extension can still provide an estimated size? We create a lookup table.

The Problem: Data Availability and the Solution

Let's be real, guys, not every listing comes with perfect data. Sometimes, the JSON data is missing. Other times, the OCR (Optical Character Recognition) process might fail. We need a way to fill in the gaps and still give our users a good idea of the size of a property. That's where our fallback system comes in. The good news is that we already have a ton of training data. We can use this data to create a lookup table. This table will be based on the postcode district and the number of bedrooms. Let's see how that looks.

Creating the Lookup Table

Here is how we will approach this. We're going to generate a lookup table that uses the postcode district and the number of bedrooms to estimate the size. It is pretty simple and works like this. We will group all the properties by their postcode district and number of bedrooms, and then calculate the median size. This will give us a good estimate for each combination. Here's a Python code example:

# Generate from training data
size_lookup = df.groupby(['postcode_district', 'bedrooms'])['size_sqft'].median().to_dict()

# Example output
SIZE_BY_BEDS_POSTCODE = {
    ('SW1', 1): 550,
    ('SW1', 2): 850,
    ('SW1', 3): 1200,
    ('SW3', 1): 500,
    ('SW3', 2): 780,
    # ... ~200 entries
}

This lookup table will be our secret weapon. We are estimating the size of a property based on its postcode district and the number of bedrooms, even if the original data is missing.

Implementing the Fallback

Now, how does this work in practice? When our extension can't find the size information, it will use the lookup table. It will take the first part of the postcode (e.g., SW1 from SW1A 1AA) and the number of bedrooms to look up the estimated size. We will also include a default fallback. If a specific postcode/bedroom combination isn't in our table, we'll use a basic heuristic: beds * 400. And of course, we'll let the user know that the size is an estimate by the inclusion of the size_source in the API.

if not size_sqft:
    district = postcode.split()[0]  # "SW1" from "SW1A 1AA"
    size_sqft = SIZE_LOOKUP.get(f"{district}_{beds}", beds * 400)  # Default heuristic
    size_source = 'estimated'

Tasks and Implementation Details

Alright, let's break down the tasks involved in getting this done:

  • Generate the Lookup Table: We will use our existing training data to create the size_lookup.json file. This is the heart of our fallback system. Here's the Python code:

    df_with_sqft = df[df['size_sqft'].notna()]
lookup = df_with_sqft.groupby(['postcode_district', 'bedrooms'])['size_sqft'].median()
lookup.to_json('size_lookup.json')

  • Deploy the JSON file with the API: This involves making the lookup table accessible through our API. We need to make sure the extension can access the data it needs.

  • Implement the Fallback in the API: This is where we implement the logic we discussed earlier. If the size isn't available from the primary sources, we use the lookup table. If there is no lookup, we use our default. It's a two-step process to ensure a result every time.

  • Return Size Source in API response: The API response will now include a size_source field, indicating whether the size came from the extension (original data) or was estimated.

Acceptance Criteria

Here's what we need to achieve to call this a success:

  • Lookup Table Coverage: The lookup table should cover at least 80% of London postcodes. This ensures that the fallback mechanism works for most properties.

  • Default Fallback: For any unknown postcodes, our default heuristic (beds x 400) should kick in, providing a reasonable estimate.

  • API Response: The API response must clearly indicate when the size was estimated, giving the user transparency.

This is a team effort. We'll be generating the lookup table from existing training data. Then, we need to deploy the JSON file with our API. Finally, we'll implement the fallback in our API and make sure the API response indicates when the size has been estimated.

Benefits and Impact

This enhancement offers several benefits:

  • Improved User Experience: Users will get more complete information, even for listings where size data is initially missing.

  • Increased Data Coverage: We'll provide size estimates for a wider range of properties, giving our users a more comprehensive view of the market.

  • Transparency: Users will know when the size is estimated. We are building trust.

This is all about providing our users with the best possible data, even when the original data is unavailable. It is a win-win for everyone involved!