The Pattaya Market, Measured From 4,903 Distinct Listings

Most descriptions of the Pattaya market are adjectives. This one is a count. In September 2026 I pulled the city's condominium inventory, deduplicated it, and published what was left: 4,903 distinct listings across 320 named buildings. Everything below is derived from that file, and every figure says how many rows stand behind it.

How large is Pattaya's condominium market once duplicate listings are removed?

4,903 distinct listings, across 320 named buildings, from a September 2026 pull capped at ฿12,000,000. The raw pull was 8,125 rows. The difference between those two numbers is the whole reason this section exists.

The workbook behind that count is city-guides/pattaya/data/pattaya-master.xlsx, and the count itself is reproducible in one command from scripts/listing-counts.py. That matters more than it sounds. A market-size figure that nobody can re-derive is a claim, and claims age badly. This one can be checked by running the script against the file, and if the site ever disagrees with the script, the site is wrong.

Sitting Pattaya alongside the rest of the dataset gives the honest scale. The four cities together come to 33,030 distinct Thai listings: Bangkok 12,097, Phuket 11,997, Pattaya 4,903 and Chiang Mai 4,033. Pattaya is the third-largest of the four and carries roughly 15 percent of the total. It is a substantial market with a narrow published footprint, and the rest of this page is about why those two things are both true.

Why does the row count differ from the listing count?

Because a collection pass appends to what is already there, so the same listing is read more than once across passes. 8,125 rows contain 4,903 distinct listings. The 3,222 difference is repetition, not inventory, and publishing the row count as a market size would overstate Pattaya by 66 percent.

Deduplication sounds like a detail and is actually the single easiest place to publish a wrong number. The obvious method is to collapse on the source URL: one URL, one listing. On this dataset that method is wrong, and it is wrong in a way that destroys real inventory rather than trimming duplicates.

The reason is that a URL records where the collector was, not which listing it read. A search-results URL can be visited many times and return different properties every time. Collapse on the URL and every one of those properties becomes a single row. So the key used here is a composite of four fields together: source URL, building name, price and floor area.

• A repeated listing URL with identical rows behind it is one listing, and the composite key collapses it correctly.
• A repeated search URL with different rows behind it is many listings, and the composite key keeps every one of them.

I am spelling this out because I got it wrong twice before getting it right, in both directions, and the corrections are in the repository's commit history rather than quietly overwritten. The published figure for Pattaya is 4,903 and the method that produces it is the composite key.

How many buildings survive rent validation, and why so few?

320 named buildings appear in the sale data. 147 carried at least three units and were assessed. 51 were published and 96 were cut, a 65 percent cut rate. Ninety-one of those 96 were cut for one reason: their rent evidence came from a single platform with nothing to check it against.

The rent side is a separate collection from the sale side. It returned 13,583 retained comparables across 729 named Pattaya buildings, and the distribution of confidence across those 729 is the whole story: 57 buildings reached multi-verified (two or more platforms agreeing within 20 percent), 11 were mild-divergent (two or more platforms, 20 to 40 percent apart), and 654 were single-source.

A single asking rent on one platform is not evidence. It is one person's number. The standard applied here is that a building enters the published file only when independent platforms are asking roughly the same thing for it, which is why 654 buildings with real listings behind them are absent from the file. They are not bad buildings. They are unevidenced ones, and those are different words that a brochure would treat as the same.

What those 51 buildings net, catchment by catchment with the negative rows left in, is set out in Pattaya rental yield. How they rank against each other is in the best-areas ranking.

How is Pattaya's inventory distributed across its tambons?

Very unevenly. Nong Prue holds roughly 81 percent of all distinct listings and Na Kluea a further 17 percent. Everything else in the pull comes to about 1 percent combined, and three tambons returned too few listings to report a median at all.

September 2026 pull, 4,903 distinct listings across 320 named buildings, capped at ฿12,000,000. Buildings are counted separately from listings. Shares are rounded and do not sum to exactly 100. Areas are tambons, the administrative sub-district a building sits in, because that is the unit the data can evidence.

Two things follow from that distribution, and they pull in opposite directions. The first is that Pattaya is not really five markets. It is one large one and one medium one, with a tail too thin to describe. The second is that the concentration is what makes the published file usable at all: 81 percent of the inventory sitting in a single tambon is also 3,983 listings of depth in one place, which is more than most cities offer in their best catchment.

The tambon boundaries do not match the names people use. Pratumnak and much of Jomtien both sit inside Nong Prue and cannot be separated out on the evidence, which is why the file reports two catchments rather than the four or five a listing site would show. That decision, and what it costs, is argued in the Jomtien and Pratumnak page. Asking-price medians per tambon are published separately in Pattaya condo prices by area.

How many platforms stand behind the Pattaya sale data?

Two, and they are very unequal. Of 8,125 sale rows, 8,094 came from one platform and 31 from a second. The rent side is broader at three platforms, which is the only reason cross-source validation was possible at all.

This is the most important caveat on the page and the one most likely to be skipped. A sale dataset that is 99.6 percent one source is a dataset with one source's coverage, one source's geographic bias and one source's listing conventions baked into it. Where that platform is thin, my file is thin, and I have no independent way to know where those places are.

The contrast with the island market is instructive. Phuket's equivalent workbook draws on five sale-side platforms, and that breadth is visible downstream in how many distinct areas resolve into reportable catchments. The full comparison is in Pattaya versus Phuket.

The rent collection is the better-covered half: 10,110 comparables from DotProperty, 2,779 from FazWaz and 998 from Thailand-Property. Three platforms is a workable minimum for cross-checking and it is not a comfortable margin. It is the reason 57 buildings reached multi-verified rather than 200.

What does the absence of an urban rail network do to catchment logic here?

It removes the organising variable the Bangkok analysis is built on. The transit-distance column in the Pattaya workbook is empty for all 8,125 rows, so catchments here are drawn on administrative tambon boundaries instead, and proximity has to be reasoned about rather than measured.

In Bangkok, distance to a station is the single strongest organising fact about a condominium. It sorts price, it sorts the tenant pool, and it is measurable to the metre. The Bangkok catchment work leans on it heavily because it is there to lean on.

Pattaya has no equivalent. What it has instead is a coastline, and position relative to it does most of the sorting. But a coastline is a gradient rather than a set of points, and there is no column in the data that records a unit's distance to the water. So the honest position is that the file reports what it can evidence, tambon by tambon, and declines to publish a beachfront premium it cannot measure.

That gap has a consequence worth naming. Two units in the same tambon, one a short walk from the water and one a long way inland, appear in the same bucket. The median absorbs both. Anyone using a tambon median as a proxy for a specific address will be wrong by a margin the data cannot quantify, which is the strongest argument on this page for reading the building rather than the area.

How does Pattaya compare with Phuket on inventory depth?

Phuket is roughly two and a half times deeper on listings and nearly five times deeper on named buildings: 11,997 distinct listings across 1,515 named buildings, against Pattaya's 4,903 across 320. On published buildings the gap narrows sharply, to 61 against 51.

That narrowing is the interesting part. Phuket enters the validation stage with almost five times the building count and finishes with about 20 percent more published buildings. Pattaya converts a much higher share of what it starts with, because its inventory is concentrated in large towers where three or more units on the market at once is ordinary rather than exceptional.

The unit counts make the same point from the other side. Pattaya's 51 published buildings carry 2,233 units between them; Phuket's 61 carry 1,235. Pattaya's file is shallower in coverage and deeper in each thing it covers, and which of those matters depends entirely on whether a buyer is surveying a market or underwriting a building.

The full side-by-side, with entry prices and net ranges kept in separate sections, is in Pattaya versus Phuket.

What does this dataset not cover, and why?

Condominiums only, asking prices only, capped at ฿12,000,000, from one dominant sale platform, in September 2026. It contains no transacted prices, no villas or landed housing, nothing above the cap, and no measurement of distance to the beach.

The six limits, stated plainly

• Asking, never achieved. Every price and every rent in this file is what the market is asking. No public source holds transacted condominium prices at this granularity, so an asking median is a ceiling to negotiate against and not a valuation.
• Condominiums only. Villas and landed housing are excluded, because a foreign buyer's direct freehold route runs through a condominium unit inside the building's 49 percent foreign quota. How that works is a separate page.
• A ฿12,000,000 ceiling. The pull is capped, so the top of the market is absent by design. Nothing on this page describes the segment above it.
• One dominant sale source. 8,094 of 8,125 rows. Where that platform's coverage is thin, so is mine, and I cannot tell you where.
• Three tambons too thin to report. Bang Lamung returned 45 listings and is indicative only; Nong Pla Lai and Sattahip returned four each. The counts are published rather than the medians, because four listings is a number and not a market.
• A dated snapshot. September 2026. Foreign-quota markers were observed at the time of the pull and are not confirmations. Quota is per building and slots move; the only body that can state the current position is the building's own juristic office.

None of that makes the file less useful. It makes it a file with known edges, which is the only kind worth underwriting from. The rest of this research line applies the same standard to the other three cities: Bangkok, Phuket and Chiang Mai, with the regional versions in Bali, Vietnam and the Philippines.