Decoding price-product-placement-promotion elasticity for Real Estate assets

Menhir helps companies understand and segment target customers, in order to address each population with the right feature-pricing mix


• Real Estate asset marketing is performed by Servicers with little granularity due to high volumes and fast pace of management

• This leads to increased costs as decisions cannot be drilled down to the particular cases. The answer to “which channel is best?” for an asset is usually “all channels”

• Following this strategy sets a performance baseline, but costs are way above what it is really needed to maintain that performance

The Challenge

• The customer was spending +1.8M€ in asset publishing, as the strategy was to publish all assets in all Real Estate portals

• The goal was to maintain lead generation performance while reducing publishing costs

• Real Estate asses are sold on a 1:1 ratio. Generating 1,000 leads for an apartment does not imply the potential of selling 1,000 apartments, as assets differ from one another

The Approach

Liquidity model: ML model to anticipate, for an asset with certain features (including price), the amount of leads that it could generate in the market

Placement model: ML model to predict which would be the Real Estate portal that would bring the lead that acquires the asset

Saturation pace model: model that identifies the pace at which each asset saturates lead generation. Lead generation follows a log curve, where 80% of leads are generated in the first X days

Cascading algorithm: algorithm that generates a publishing cascade based on the combined output of the previously mentioned models, with the goal of maintaining asset sales while reducing publishing costs

The results

• Menhir identified a potential cost reduction of 300.000€ for the assets under consideration (-16% on last year’s budget)

• Cascade implemented for asset inflow

Stay tuned.

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