Workplaces & Employment Hubs Near Any Plot
Where the jobs are — industrial estates, IT parks and the companies operating around your plot, from owned records plus a live view.
Why workplace access decides land value
People buy homes where the jobs are. In India's cities the single biggest driver of residential land demand — ahead of almost everything except price and road access — is how close a plot sits to employment: the IT corridor, the industrial estate, the office cluster people commute to every day. Yet that distance is almost never on the table when you're evaluating a parcel.
It’s an information problem. A GCC campus, a TSIIC industrial estate, and the fifty software companies around them all live in different places — Google, a state industrial registry, OpenStreetMap — and no single view puts them on one map against your plot. This tool does: it answers “how close is this land to where people work?” for any parcel, then lets you verify the specifics.
Why proximity to jobs moves the price
- Rental demand: plots within a short commute of an IT park or industrial estate carry the deepest tenant pool — the working population that drives rental yield and resale liquidity.
- Appreciation: land on the growth side of a workplace / employment hub (Gachibowli, Kondapur, the ORR industrial corridors) appreciates with the jobs it feeds, not just with the city.
- End-use fit: a plot beside an industrial estate suits warehousing or worker housing; one beside a tech park suits residential or commercial — the neighbours tell you the highest and best use.
The numbers here are a first assessment from openly available and owned data — always confirm on the ground before a purchase decision.
What this tool tells you
Four readings, in the order they matter when you are buying land.
Straight-line distance to the closest employment hub, and its name. This is what drives the verdict — commute and rental demand both follow it.
Industrial estates recorded by TSIIC, grouped into clusters with their plot count and area. The authoritative, owned employer record.
Mapped IT and tech-park boundaries from OpenStreetMap. A plot inside one is inside an employment hub, not merely near it.
The nearest operating companies, live from Google — the immediate surroundings the owned record cannot cover. Shown, not stored.
Three sources, and why each is here
Each fills what the others cannot.
TSIIC — the owned record
Government industrial-estate data, allotted plot by plot. Durable, exportable, and the only source that tells you how big an employment cluster actually is.
OpenStreetMap — the mapped boundaries
IT and tech parks as drawn polygons, so a plot can be tested against the hub’s real edge rather than a centre point.
Google — the live slice
Companies operating right now, including at fringes the owned data has not caught up with. Live means current; it also means shown rather than stored.
Methodology and data sources
Owned data, and a live layer kept apart
TSIIC (Telangana State Industrial Infrastructure Corporation) industrial estates and allotted plots, grouped into estate clusters, plus OpenStreetMap IT parks (ODbL). Both are stored, numbered to match the maps, and included in the GeoJSON download. Google Maps supplies the live nearby-company list, which is shown with attribution per provider policy and is not stored or exported.
Why the maps come from Google
Google Places points may only be rendered on a Google map, so this tool’s three radius views (10 km, 2.5 km, 500 m) are Google Static Maps rather than the satellite tiles the rest of the report uses. Owned areas are shaded and carry numbered pins; live Google places are small dots, numbered in their own table because a small marker cannot carry a label.