On this page
- What RERA project data actually contains
- Mapping competitor land banks from RERA registrations
- Extracting pricing signals from RERA disclosures
- Using RERA data for site sizing and FSI analysis
- Identifying supply gaps and underserved micro-markets
- Practical workflow: RERA data to land bid
- Limitations of RERA data for land analysis
RERA project data is one of the most underused public datasets in Indian real estate. Every registered project in the country must disclose land area, unit count, promoter details, project timelines, and quarterly progress — all available for free on state RERA portals. For teams using land acquisition software, this data answers critical questions: who is buying land in your target micro-market, how much they paid per square foot of built-up area, and where supply gaps exist.
This guide shows you how to systematically extract, structure, and apply RERA data to land deal decisions. The workflow is:
- Identify the target micro-market (typically a 500m–2km radius around your candidate parcel).
- Pull the RERA project list for that area from the state portal.
- Extract key fields — land area, unit count, sanctioned FSI, promoter, registration date, completion date, price disclosures.
- Analyse along four lenses:
- Supply — unit counts and absorption rates across registered projects
- Pricing — price per sq ft trends across recent registrations
- Competition — promoter activity and land-bank concentration
- Site sizing — FSI consumed and land-area-to-buildable ratios
- Synthesise into an investment decision — bid range, deal terms, and timing.
What RERA project data actually contains
State RERA portals publish structured data on every registered residential and commercial project. The mandatory disclosure fields include promoter name, project site address, total land area (in sq. ft. or sq. m.), number of buildings and units, sanctioned FSI, carpet area per unit type, project start and completion dates, and the bank account holding buyer funds. Quarterly Progress Reports (QPRs) add construction status, units sold, and financial utilisation data.
The depth of disclosure varies by state. MahaRERA publishes the most complete dataset — including approved layout plans, commencement certificates, and even the land title documents submitted by the promoter. TNRERA provides good project-level data but fewer supporting documents. KRERA (Karnataka) and RERA Gujarat fall somewhere in between.
Fields most useful for land acquisition:
| Field | What It Tells You |
|---|---|
| Total land area | Actual parcel size the developer acquired |
| Number of units | Density and product type (plotted vs apartment) |
| Sanctioned FSI | What the planning authority approved — reveals achievable density |
| Project start date | When the developer likely closed the land deal (typically 6-12 months prior) |
| Promoter name | Who’s active in the micro-market |
| Carpet area breakdown | Product mix and target buyer segment |
| QPR completion % | Whether the project is progressing or stalled |
Mapping competitor land banks from RERA registrations
RERA data is the most reliable public source for tracking which developers are actively acquiring land in a given area. Every new project registration signals a recent land purchase. By filtering RERA registrations by location and promoter, you can build a near-complete picture of competitor land banks.
Start by pulling all registered projects within your target micro-market from the state RERA portal. Group them by promoter name. For each promoter, sum their total registered land area, note the registration dates, and track the project status. A developer with 3 registered projects totalling 15 acres in a single suburb — all registered within the last 18 months — is clearly building a position in that market.
What to look for:
- New entrants: Promoters registering their first project in a micro-market they haven’t operated in before. This signals expanding demand and rising land values.
- Serial acquirers: Developers with 3+ registrations in the same area within 24 months. These players have likely locked in land at lower rates and will dominate local supply.
- Stalled projects: QPRs showing less than 20% completion after 2+ years. These can signal distressed assets and potential land resale opportunities.
This kind of competitor intelligence at the micro-market level is what separates informed land bids from guesswork. When you know that three major developers have already acquired 40 acres in a corridor, you can assess whether additional supply will be absorbed or will saturate the market.
Extracting pricing signals from RERA disclosures
RERA project data can serve as a pricing benchmark for land acquisition when combined with basic reverse calculations. While RERA does not directly publish land acquisition costs, it publishes carpet area, unit pricing (in some states), and total project area — enough to derive implied land values.
The approach works as follows. Take a RERA-registered project in your target area. From the disclosure, note the total land area (say, 2 acres / 87,120 sq. ft.), total carpet area across all units (say, 1,50,000 sq. ft.), and the average selling price per sq. ft. (say, INR 6,500). Multiply carpet area by selling price to get gross revenue: INR 97.5 crore. Apply standard cost ratios — land typically accounts for 20-35% of residential project cost in Tier 1 cities — and you get an implied land cost of INR 19.5-34.1 crore, or roughly INR 2,240-3,915 per sq. ft. of land area.
This is a rough estimate, but when you repeat it across 10-15 projects in the same micro-market, you get a reliable pricing band. Cross-reference this with guideline values from the registration department and recent sale deed data to triangulate a fair land price.
For faster analysis, AI-powered pricing intelligence for land acquisition can run these calculations across hundreds of projects simultaneously and flag outliers.
Using RERA data for site sizing and FSI analysis
The sanctioned FSI in RERA disclosures tells you exactly what the planning authority approved for a comparable site. This is critical for site sizing — it determines how many saleable square feet you can build on a given parcel, which directly drives your land bid.
For any target parcel, pull RERA registrations for projects on adjacent or similar-zoned plots. Check their sanctioned FSI. If 8 out of 10 comparable projects received FSI of 2.5, you can reasonably assume your parcel will get the same. If one project received FSI of 3.5, dig into why — it may have purchased additional TDR (Transfer of Development Rights) or fall under a special planning zone.
A worked example for a 2-acre target parcel:
| # | Step | Calculation | Result |
|---|---|---|---|
| 1 | Target parcel | — | 2 acres |
| 2 | Comparable RERA FSI median | 8 of 10 comps at 2.5 | FSI 2.5 |
| 3 | Buildable area | 2 × 43,560 × 2.5 | 2,17,800 sq ft |
| 4 | Efficiency factor | typical 70% for mid-rise | 70% |
| 5 | Saleable carpet | 2,17,800 × 0.70 | 1,52,460 sq ft |
| 6 | Revenue estimate | carpet × market rate | varies by submkt. |
| 7 | Residual land value | revenue − cost − margin | your bid ceiling |
Key ratios to extract:
- FSI utilisation: Sanctioned FSI vs. available FSI under the development control rules. Under-utilised FSI means the developer left money on the table — or the site has constraints.
- Ground coverage: Total built-up footprint as a percentage of plot area. Compare this across projects to understand typical setback and open space norms.
- Unit density: Number of units per acre. A project with 80 units per acre targets a very different buyer than one with 20 units per acre.
- Carpet-to-land ratio: Total saleable carpet area divided by total land area. In Mumbai, this can exceed 5:1. In Chennai suburbs, it’s typically 1.5:1 to 2.5:1.
Identifying supply gaps and underserved micro-markets
RERA registration data, when aggregated by location, reveals where new housing supply is concentrated and where it’s absent. Micro-markets with high population growth but low RERA registrations represent potential acquisition opportunities.
Pull the total number of RERA-registered projects and units for each micro-market in your target city over the last 3 years. Compare this against demand proxies: population growth (census data), employment centres (IT parks, industrial corridors), and infrastructure projects (metro lines, highway expansions). A micro-market with a new metro station, two IT parks, but only 500 RERA-registered units in 3 years is undersupplied.
Supply gap indicators from RERA data:
- Low registration volume: Fewer than 5 new project registrations in 3 years in a growing micro-market.
- High absorption rates: Projects where QPRs show 70%+ units sold within the first year of registration. Demand is outpacing supply.
- Product type gaps: All registered projects are 2BHK and 3BHK apartments, but no plotted developments or villa projects. The missing product type may have unmet demand.
- No premium segment: Average carpet area across projects is below 1,000 sq. ft. If the area’s demographics are shifting upmarket, there’s room for a premium project.
Combine RERA supply data with location intelligence — infrastructure mapping, population density, and employment data — to build a complete picture of where to acquire next.
Practical workflow: RERA data to land bid
Turning RERA data into an actionable land bid requires a structured workflow. Here is the process that works for acquisition teams evaluating 10+ parcels per month.
Step 1: Define the search area. Pick a 3-5 km radius around your target parcel. If you’re evaluating land in Chennai, for example, define the micro-market by suburb or revenue village name.
Step 2: Pull all RERA registrations. Go to the state RERA portal, filter by district and location, and export the project list. MahaRERA allows CSV export. For states without export, you’ll need to manually extract or use a scraping tool.
Step 3: Build a comparison table. For each project, capture: promoter name, registration date, total land area, total units, sanctioned FSI, carpet area breakdown, and QPR status. You need at least 8-10 comparable projects for a meaningful analysis.
Step 4: Run the analysis.
- Calculate average FSI utilisation to estimate your buildable area.
- Reverse-calculate implied land values from project pricing.
- Map competitor positions by promoter and land area.
- Identify supply gaps by comparing registration volume to demand indicators.
Step 5: Set your bid range. Your residual land value — gross revenue minus construction cost, marketing, finance cost, and profit margin — gives you the ceiling. RERA-derived pricing benchmarks give you the floor (what others paid). Your bid should fall within this band, adjusted for site-specific factors like access, shape, and title clarity.
Step 6: Monitor ongoing. New RERA registrations in your target area change the supply dynamics. Set up monthly checks to catch new entrants and adjust your strategy.
Limitations of RERA data for land analysis
RERA data is powerful but not complete. Understanding its limitations prevents overreliance on a single data source.
Data quality varies by state. MahaRERA is the gold standard. Some smaller states have portals with incomplete records, broken search, or data that hasn’t been updated in quarters. Always verify the QPR date before trusting project status.
Registration lag. Developers register with RERA after acquiring land and obtaining necessary approvals — typically 6-18 months after the land deal closes. RERA data reflects past acquisitions, not current market pricing.
No land cost disclosure. RERA requires disclosure of project costs but not the specific land acquisition price. You can reverse-calculate estimates, but these are approximations.
Plotted developments have less data. Plotted layout projects (common in South India) have simpler RERA filings with less granular data than apartment projects.
Commercial projects are underrepresented. Many states have lower registration compliance for commercial projects. Don’t assume RERA data captures the full commercial supply pipeline.
Despite these limitations, RERA data remains the most structured, freely available dataset for understanding real estate supply at the micro-market level. Combined with revenue records, registration data, and satellite imagery, it forms a solid analytical foundation for land acquisition decisions.
For a unified approach to managing your entire pipeline, explore Proquiro’s land acquisition software for Indian real estate teams.