China Spring, Texas Airbnb & short-term rental market data

Name: China Spring, Texas short-term rental overview data
Creator: AirDNA
License: https://www.airdna.co/terms

Updated September 29, 2026By The AirDNA Data Team

China Spring, Texas has 923 active short-term rental listings that earn an average of $36.3K per year, with 52% average occupancy and a $208 average daily rate.

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AirDNA tracks daily performance across 10M+ Airbnb, Vrbo, and Booking.com listings in 120K+ global markets. How we collect this data →10M+ listings across Airbnb, Vrbo & Booking.com in 120K+ markets. How we collect →

China Spring short-term rental market summary

The China Spring, Texas Airbnb market at a glance.

China Spring's short-term rental market has 923 active listings as of August 2026. The average active listing earned $36.3K in revenue over the trailing twelve months. Listings were booked 52% of nights they were available at an average daily rate of $208. RevPAR (daily rate weighted by occupancy) was $108. From August 2025 to August 2026, revenue is up 135.3%, occupancy is up 69.4%, ADR is down 15.9%, RevPAR is up 41.0%, and active listings are up 2537.1%.

AirDNA Market Score

Rental demand75
Seasonality93
Revenue growth73
Investability92
Regulation73

How Market Score is calculated

Analyst's take

China Spring's AirDNA Market Score: 97 out of 100.

China Spring, Texas scores 97 out of 100 on AirDNA's Market Score, a 40-to-100 scale benchmarked against other short-term rental markets in the same country with at least 15 active listings.

The score combines 5 subscores, each computed from observed market data:

Rental demand (75): annual occupancy combined with year-over-year listing growth.
Seasonality (93): the percentage gap between the lowest and highest monthly average revenue over the past year (a smaller swing scores higher).
Revenue growth (73): the year-over-year change in RevPAR, counting only properties booked in both periods.
Investability (92): local home prices relative to what short-term rentals in the area earn.
Regulation (73): signals of short-term-rental rules inferred from host and property behavior.

The Market Score is a starting point, not an investment decision.

Market-level averages hide wide variation. Here's how to go deeper in the app:

1Narrow to a neighborhoodNeighborhood metrics give a far more accurate picture of the exact area you're considering.
2Apply filtersFilter by bedrooms, price tier, amenities, and reviews to reach properties that actually match yours.
3Estimate one addressProject what one specific property can earn with a custom comp set built from its exact specs.

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Key definitions

How AirDNA defines these metrics

Active listings: Active listings are the short-term rentals on Airbnb, Vrbo, and Booking.com that were available or booked in the trailing 12 months. AirDNA de-duplicates the same home across channels, and the count refreshes monthly.How AirDNA counts active listings →
Annual revenue: Annual revenue is the trailing-twelve-month earnings of a typical active listing before host expenses, including booked nightly rates plus cleaning and other guest fees.How AirDNA calculates revenue →
Occupancy: Occupancy is the share of available nights that get booked, averaged across active listings over the trailing 12 months. It measures real demand, not how many nights a host chooses to list.How AirDNA calculates occupancy →
Average daily rate (ADR): ADR is the average nightly price guests actually paid across booked nights in the trailing 12 months. Nights that didn't book are excluded.How AirDNA calculates ADR →
RevPAR: RevPAR (revenue per available rental) is occupancy multiplied by ADR. It captures both how often a listing books and what it charges, making it the truest measure of earning power per available night.What is RevPAR? →
Market Score: AirDNA's Market Score grades each market from 40 to 100 on short-term rental potential, weighing rental demand, revenue growth, seasonality, regulation, and investability. Markets are benchmarked against others in the same country.How Market Score is calculated →