Indiana Commercial Comparable Sales: Working with Statewide SDF Data

What Indiana's SDF dataset is

Every real property transaction in Indiana requires a Sales Disclosure Form (State Form 46021), filed with the county auditor and rolled up statewide by the Department of Local Government Finance. Unlike an MLS listing, an SDF record isn't a marketing artifact -- it's a disclosure filing tied to the actual conveyance, and it carries fields an MLS record typically doesn't: the property's DLGF property class code, assessed value at time of sale, parcel number, township, and a set of flags the auditor's office uses to characterize whether the sale looks like a normal arm's-length transaction.

For commercial appraisal work, that makes SDF data the closest thing Indiana has to an authoritative statewide record of what actually sold, to whom, and for how much. There's no equivalent commercial MLS with mandatory price disclosure across the state -- SDF data is the primary source because in most cases it's the only source with a verified sale price.

The challenge: 92 counties, no unified interface

The friction isn't the data itself -- it's getting at it. SDF records exist, but the state doesn't offer a built-in comparable-sales search for commercial properties. Each of Indiana's 92 counties runs its own assessor's office, and while SDF filings roll up to DLGF's Gateway system, using that data for actual comp analysis means downloading raw files, parsing them, and filtering for the property types and date ranges you care about -- work that has to be redone for every search.

County assessor portals vary widely in quality, format, and how (or whether) they expose building characteristic data alongside the sale record. Some counties are on modern GIS platforms with clean parcel lookups; others still run legacy systems where getting a property record card means navigating an unfamiliar interface county by county. An appraiser working a multi-county assignment, or comparing sales across county lines, ends up doing that integration work manually every time. That's the actual pain point -- not a lack of data, but a lack of anywhere to search across it.

Filtering for valid comparable sales

Not every SDF record is usable as a comp. The form itself captures information relevant to whether a sale reflects true market value -- and Indiana's assessment system generates flags for sales that look non-market: transfers between related parties, foreclosures and deeds in lieu, sales involving a government entity, and other transaction types that don't reflect arm's-length negotiation between a willing buyer and seller.

Filtering on these flags before treating a sale as a comparable is standard practice, not optional due diligence -- a related-party transfer or a distressed sale can distort a comp grid meaningfully if it's included without adjustment or exclusion. The practical skill is less about knowing that these flags exist and more about knowing which combinations of flag and property type are worth a second look versus an automatic exclusion, since not every flagged sale is actually unusable and not every clean-flagged sale is automatically reliable.

See our analysis of Indiana commercial sale velocity by county for data on comp availability across all 92 Indiana markets.

Property class codes for commercial sales

Indiana's DLGF Code List 1 assigns every parcel a property class code that determines how it's categorized for assessment purposes, and that same code is the practical filter for isolating commercial sales from residential, agricultural, and industrial ones. In broad terms, the codes run in blocks by land use -- agricultural uses sit in the 100s, industrial uses occupy part of the 300s, commercial uses run through the 400s (with sub-codes distinguishing, for example, small retail from a shopping center from a restaurant or a gas station), and residential uses sit in the 500s.

Knowing the block structure matters because a lot of commercial comp searches actually need several adjacent codes, not one -- "retail" comparables might reasonably span several 400-series codes depending on how granular the search needs to be. Filtering by the wrong single code, or missing an adjacent one, is a common way to under-collect a comp set without realizing it.