Find India Real Estate Comparables With SquareYards Listings

Comparable-property analysis needs more than the five nearest listings. The properties should share locality, transaction type, bedroom count, approximate area, and a recent observation date.

Use the SquareYards Property Scraper to collect sale or rental listings by city and locality. Store the query and collection date with every row.

Normalize area and price

Keep the original strings, then create numeric columns only where the unit is clear.

df["price_num"] = pd.to_numeric(df["price"], errors="coerce")
df["area_num"] = pd.to_numeric(df["area"], errors="coerce")
df["price_per_area"] = df["price_num"] / df["area_num"]

If the source mixes square feet and square metres, normalize units before division. Drop impossible outliers into a review table instead of silently clipping them.

Match the comparable set

Filter to the same property_for, locality, property type, and bedroom count. Use an area band, such as plus or minus 20 percent. Deduplicate on canonical URL and flag repeated project names with near-identical descriptions.

Report median asking price per square foot, the interquartile range, listing count, and observation dates. The median is less sensitive to premium penthouses and obvious data errors than the mean.

Keep the market caveat visible

Portal listings are asking prices. They can be stale, duplicated across brokers, or intentionally optimistic. For investment or lending decisions, combine the result with registered transaction data and on-the-ground verification.

Used carefully, the dataset is a fast way to screen localities, spot unusual listings, and decide where deeper valuation work is justified.