Why we built India's anonymous rent map

Every renter knows the moment. You're standing in a flat you like, the broker senses it, and a number lands on the table. Is ₹52,000 fair for this 2BHK in HSR? The broker says it's a steal. The listing said ₹49,000 last week. Your colleague pays ₹40,000 for "something similar", wherever that is. You have thirty seconds and zero information, and the next eleven months of your budget hang on the guess.

We built MyBharat because that moment is ridiculous. Rent is the largest single expense in most urban Indian households, and it is negotiated in an information vacuum that would be considered market failure anywhere else. Stock trades at nanosecond transparency; a flat — worth more per year than most portfolios — trades on vibes.

The asymmetry problem

Think about who knows what in a rental negotiation.

The landlord knows the last three rents this flat fetched, what the neighbour's tenant pays, what the society's going rate is, and how desperate they are. The broker knows what's closed recently on both sides of the street. You know a listing price, which is a wish, and whatever a friend remembers.

Markets with this kind of one-sided information don't clear at fair prices — they clear at whatever the informed party can extract. That's not a moral failing; it's just what happens when one side can see and the other can't. Every Indian city has its own version of the outcome: Bengaluru's premium societies quietly ratcheting deposits, Mumbai's "2 months is for newcomers, everyone pays 6" folklore, Pune newcomers discovering they're the only flat in the building paying full asking.

The fix isn't outrage. It's data on the other side of the table.

Why asking prices weren't enough

The obvious objection: portals already publish rent data. They don't. They publish asking prices — the listing number before negotiation, before staleness sets in, before the flat that never rents at that price quietly comes down. Asking prices answer "what do landlords want?" The question every renter actually has is "what did people like me, in this building, finally agree to pay?"

There was nowhere to find that. Not because tenants don't know their own rent — of course they do — but because nobody had built the place for that knowledge to pool safely. And that word, safely, is the entire product design problem.

Why anonymous, and why it has to be

Ask a tenant to post their rent with their name and you get silence, and rightly so. Rent is sensitive in ways even salary isn't. Your landlord could see it. Your society group could. Your employer, your extended family, the next broker deciding what to quote you — all of them can read a public post, and none of them have your interests at heart.

The landlord who discovers their tenant reported a below-market renewal rate has every incentive to correct that at renewal. The tenant reporting an above-market one just told their professional network they overpay. Any identifiability warps the data at exactly the moments it matters most.

So anonymity isn't a feature we added. It's the precondition for the data existing at all. Reports on MyBharat carry no name, no login trail tying them to you, no listing, no flat number. What they carry is the substance: city, locality, society, configuration, rent, deposit. That's enough to build a map, and not enough to build a grudge.

What eight and a half thousand strangers built

The experiment worked. As of August 2026, 8,590 first-hand rent reports cover 16 cities, and the depth now genuinely surprises us.

HSR Layout alone has 796 reports — enough that its ₹38,000 median with a ₹25,000–₹57,425 middle band is probably the most reliable neighbourhood rent figure published anywhere in India. Marathahalli follows at 670 reports. But the shape of the dataset matters as much as the size. Prestige Shantiniketan in Whitefield has accumulated 25 reports putting its median at ₹80,000 — society-level resolution, the thing no portal offers, because tenants who never post anything else will spend thirty seconds on an anonymous form that helps the next person not overpay.

That's the quiet insight underneath the whole project: the knowledge already exists, distributed across lakhs of heads, each holding one rent. Aggregation isn't invention; it's plumbing. Connect the drops and you get a level.

Medians, not averages — a small honesty

Two design choices were non-negotiable once we thought about them. First, medians everywhere, never averages: one ₹2 lakh penthouse shouldn't move the "typical" rent of a middle-class neighbourhood. Second, transparency about weakness: every figure shows its report count and freshness. A median built on three reports is honest about being three reports. Bandra West's ₹87,000 across n=3 is a real rent, and it is presented as exactly as provisional as it is. We'd rather earn trust with visible caveats than manufacture false confidence — the incumbents already do the second thing well.

What we hope changes

Near-term, boringly practical things. A tenant walks into that HSR flat knowing the last 796 data points, opens the negotiation at ₹48,000 against a ₹57,425 ceiling, and saves a month's worth of groceries annually. A Pune newcomer checks society pages and declines the six-month deposit because the documented norm is 1.6–2.9. A renewal conversation starts from the spread, not the landlord's memory.

Longer-term, we'd like the map to do what good public data does: quietly raise the floor for everyone. When rents are visible, arbitrary quotes get arbitrated, deposits drift toward documented norms, and the energy spent on haggling in the dark gets spent on things renters actually care about.

And in the spirit of the thing: none of this works as a gift from us. It works as infrastructure the community funds in thirty-second increments. If you pay rent in India, you're holding one data point the map needs.

How to use this

• Start with the rent map — your city's medians, spreads and report counts, no login required.
• Explore your city pages: Bengaluru, Mumbai, Delhi, Pune, Hyderabad, Chennai, Kolkata.
• Drill into societies before you visit one — see Green View Residency, Electronic City for the format.
• Make the numbers work for your paperwork too: deposit calculator and rent receipts for HRA.
• Then report your rent. Anonymously, once, thirty seconds. That's the whole ask — it's also the whole business model of fair markets.

FAQ

Is MyBharat really anonymous? Yes. Reports need no name, no verified identity, and no login tying submissions to you. We collect the rent facts — city, locality, society, configuration, rent, deposit — and nothing that identifies the reporter.

Why should I report my rent? Because someone before you did, and that's why you can see your locality's median today. Every report sharpens the p25–p75 band for the next renter. It costs thirty seconds and returns a market.

How is this different from a listings portal? Portals show asking prices of available listings. We show what tenants actually pay — transaction data, aggregated into medians and spreads with report counts shown openly.

Can my landlord find out I reported? There's nothing to find. No name, no flat number, no login trail. A report says someone in a society pays a certain rent — which describes half the building at any time.

How many reports do you have, and where? 8,590 across 16 cities as of August 2026. Bengaluru leads with 6,700+ (HSR Layout alone has 796); the other metros are filling in fast.

Keep exploring

• How to Read a Rent Map — the data literacy behind every figure we publish
• What India Actually Pays in Rent, August 2026 — the national picture the community built
• Bengaluru Rent Report, August 2026 — the dataset's deepest city, in full
• Security Deposits: What Societies Actually Ask — transparency's next frontier

What the map looks like now (October 2026)

The manifesto needed a dashboard, so here it is. As of this month the map carries 8,904 first-hand rent reports — 8,305 of them in Bengaluru, where the community started — across 26 cities with live data, plus 855 owner-posted homes and society-level medians for over 12,000 recognised buildings. What began as an argument about asking prices is now the largest body of self-reported, evidence-linked rent data in India.

The city medians over the last 180 days, all-configurations, read like this:

Notice what the report counts are telling you. Bengaluru's median rests on more than six thousand reports; Mumbai's on seventy-nine. Both are honest numbers, but they deserve different confidence, which is why every figure we publish travels with its n. The thin cities are labelled thin. That is not a disclaimer — it is the product.

The honesty mechanics, concretely

Anyone can promise "real rents only." Here is the machinery that makes it true on this map:

• Medians, never averages. One ₹2 lakh penthouse cannot drag a locality's number up. The median is the rent of the middle report — the most representative single answer to "what do people here actually pay."
• The count rides with the number. Every median on every page — city, locality, society, even configuration rows — shows n. HSR Layout reads ₹38,000 (n=796). You can judge the evidence before you trust the number.
• Ranges, not points. Localities publish p25–p75 bands, the rent span covering the middle half of reporters. It answers the question a bare median hides: how much spread is normal here?
• ~110-metre blur. Coordinates are rounded before storage. A pin proves a neighbourhood, never a home. Your landlord cannot find your flat through us.
• Outlier review. Statistically flagged pins and community reports pull a pin out of medians until a human look confirms it.
• Graduate-to-index rules. A locality page does not enter Google until it has at least three reports; a society page needs five. Numbers that cannot support a ranking do not get one. This page layer is why you will never find a one-pin "Defence Colony median ₹1.1 lakh" headline ranking from us.

What one pin unlocks

Dropping your rent takes about thirty seconds and is anonymous. What it feeds is bigger than the map you see:

• Society pages assemble themselves. Your pin joins a society-level median — wings and phases merged — with a public evidence list, config table, and typical deposit. Prestige Shantiniketan in Whitefield reads ₹80,000 from 25 reports; your building gets the same treatment at whatever its honest scale is.
• Rent alerts fire from pins. Watch an area and you get an email within thirty minutes of a new flat or rent report landing inside it. Seekers feed the map; the map watches the market for them back.
• The monthly reports get sharper. Every edition — like the September Bengaluru report — is cut from the same pins you dropped. The published analysis is only as good as the report count, and the report count is yours.

Frequently asked questions

Is it really anonymous? What do you store?

Your rent report carries a rounded coordinate (~110 m), the rent, configuration, and optional notes — not your name, flat number, or contact. Browsing needs no account at all; contributing asks for a one-time phone or email OTP purely as spam control.

Why medians instead of averages?

Averages get hijacked by outliers — one ultra-premium listing swings a whole locality's 'average'. Medians answer the question people actually ask: what does the typical renter here pay? We publish p25–p75 ranges alongside so you can see the spread yourself.

What stops fake pins?

Rate limits, statistical outlier flagging, community reporting on any pin, and human review before a flagged pin re-enters medians. Also, structurally: a fake pin has nothing to gain — there is no lead to steal, no listing to promote.

Why is my society or area missing?

Because it has not crossed our evidence threshold yet — the map refuses to publish a median it cannot support. Drop the first pin and the page assembles; it enters search once the count shows real, stable evidence.

Does any of this cost money?

No. The map, locality and society data, tools, and reports are free, and browsing needs no login. The project is funded by clearly-labelled advertising and optional listing boosts — never by selling your data, which is another reason we collect so little of it.

Methodology, sources, and how to cite

Every figure on MyBharat is a median over anonymous first-hand rent reports from a rolling 180-day window, with the report count (n) shown beside it. We publish medians, never averages, so one palace or one dump cannot move a number. Coordinates are rounded to roughly 110 metres before storage — a pin identifies a neighbourhood, never a flat. Outliers are flagged statistically and by community report, and flagged pins are excluded from medians until reviewed. Localities and societies with fewer reports than our evidence threshold are deliberately held out of search engines rather than wearing a number they cannot support. Data cuts in this article were taken in October 2026; figures shift as new reports arrive, which is the point — the live map is always the current edition.

Related reading

• Bengaluru Rent Report — September 2026 — the flagship monthly edition, cut from the full pin set
• How to read a rent map — medians, ranges, and report counts, explained with worked examples
• Security deposits in Karnataka — what the law allows versus what societies ask
• How to find a flat in Bengaluru without a broker — the channel playbook this data makes possible
• The deposit calculator — test any ask against measured norms

What we are committing to next

A manifesto should carry promises with dates. Through January 2027, the monthly city and national reports publish on schedule — three data-backed stories a week in total — and the January flagship will grade, in public, the predictions earlier editions put on record (Bengaluru's January spike call among them). Every city page graduates its evidence thresholds automatically as reports land; thin pages stay out of your search results until their numbers deserve them, and no threshold will be lowered to win a ranking. And the retention loop now runs in both directions: watch an area and the map emails you when a flat or a report opens inside it — the seekers who feed the map are the first to know when it moves. That is the whole deal: your thirty seconds of data, returned as a market you can finally see.

How is this different from asking in my building's WhatsApp group?

Scale and survival. A group gives you a few anecdotes; the map gives you the median of hundreds of reports with the evidence list attached — and the numbers persist, get outlier-reviewed, and compound into locality and city statistics your group chat will never produce. The WhatsApp group is a rumour with good intentions; the map is the same word-of-mouth with receipts.

What happens to my rent data if the site shuts down?

It stays anonymous — there is no identity attached to a pin to sell, leak, or lose, which is exactly why we collect so little. The aggregates (medians, counts, ranges) are the product, and they are public and citable; that is the entire business, and it is why the data cannot be 'monetised' behind your back.