Mapping the Philippine Housing Market From 1,500 Listings

Here's a number that stops you cold: the average listed price of a property in my dataset was around P32.8 million. That sounds like a lot, and it is. But averages in real estate are almost always misleading. A handful of luxury condos in BGC can pull the mean up by millions, while a modest house-and-lot in Bulacan sits quietly at the other end.

I got curious about this while browsing Lamudi listings one evening. Not shopping — just scrolling and wondering who actually buys these places. When I found a scraped dataset of 1,500 listings on Kaggle, covering 138 cities, I figured it was worth a proper look.

What I Built

An exploratory analysis of Philippine real estate listing data. The project cleans web-scraped property data, detects and handles price outliers, and produces city-level and property-type comparisons. I visualized price distributions, compared house-and-lot versus condo pricing, and mapped the most expensive areas against the most affordable ones.

How I Put It Together

Web-scraped data is never clean. This dataset had all the usual issues — inconsistent price formats, missing bedroom counts, duplicate listings, and some entries that were clearly test data or spam. I spent a good chunk of time just getting the prices into a usable numeric format.

Outlier detection was important here. A few listings priced at P500 million or more would wreck any meaningful average. I used the IQR method (interquartile range) to flag extreme values: anything beyond 1.5 times the IQR above Q3 got tagged for review. Most of those turned out to be commercial properties mixed in with residential listings, so I filtered them out rather than capping them.

For the city-level comparisons, I only included cities with at least 5 listings. Showing a "city average" based on a single property would be misleading. That cut brought the usable city count down, but the remaining data was much more trustworthy.

Why This Project

Housing affordability is one of those topics everyone has an opinion on but few people look at with data. I hear "real estate is too expensive" all the time. But too expensive compared to what? Compared to where? The point of this analysis wasn't to prove that housing is unaffordable — most Filipinos already know that. It was to see the shape of the market: where the gaps are widest, what types of properties dominate, and whether there are pockets of relative affordability.

What the Data Showed

Metro Manila dominates the expensive end of the market, but that's no surprise. The more interesting finding was the size of the gap. Median prices in Makati, Taguig, and Pasig were 5 to 8 times higher than median prices in cities like General Santos or Cagayan de Oro. Same country, completely different housing economies.

Condos and house-and-lot properties told different stories. Condos clustered tightly in Metro Manila with less price variation — developers price them within known ranges. Houses were all over the map, literally and figuratively. A 3-bedroom house in Quezon City might cost 10 times what a similar house costs in a Visayan city.

• Makati, Taguig, and Pasig consistently ranked as the most expensive cities
• Condo prices showed less variation than house-and-lot prices
• Provincial cities offered significantly lower entry points but with fewer listings
• The median was a much better measure than the mean for this dataset

Looking Back

1,500 listings isn't the entire Philippine housing market — it's a sample from one platform at one point in time. That's a real limitation. But even this slice reveals patterns that ring true: the Metro Manila premium, the condo-house divide, and the vast range of prices across the archipelago.

If I had access to a larger dataset with historical listings, I'd want to track how provincial prices have changed as more people consider living outside Metro Manila. The work-from-home shift might show up in the data as rising demand in places like Laguna, Cavite, and Cebu.