Tableau & PostgreSQL Developer for Real Estate Investment Analytics Dashboard

We are building a high-resolution real estate analytics dashboard to identify property-level profit spreads and optimal build locations based on public and proprietary data. To complete this dashboard, we require expert assistance in executing three core spatial analytics tasks using PostgreSQL and Tableau: Project Deliverables: 1. Smoothed Heatmap of Predicted New-Construction Home Values (2740 sf bolthires) - Generate gridded predictions (lat/lng bins or tiles) of estimated sale price for a 2740-sf home. - Weight comps by proximity to 2740 sf, year built, and optionally distance. - Apply fallback logic (e.g., add 10% premium if no new builds exist nearby). - Output in Tableau-ready format using lat_bin, lng_bin, and predicted_price_2740. - Goal: visualize regional pricing for new homes even in areas with sparse comps. 2. Underbuilt Parcel Identifier - Identify single-family parcels where the AVM value / lot size suggests underutilization (e.g. FAR well below typical). - Cross-reference AVM (property_avm.estimated_value or inferred_estimated_value) with parcel size (property_geometry) and structure size (property_structure). - Output table of bolthires parcels with parcel_id, lat/lng, lot size, building size, estimated AVM, and potential upside. 3. Spread Calculation - Sales Price Minus AVM - For all under-built lots, subtract current avm from estimated value of sale (for 2740 sf home, based on where in heat map grid it is). - Group and visualize spread distribution at parcel level, zip code level, and spatially (lat/lng grid). - Use this as a proxy for where new builds can command outsized premiums over current valuations. Apply tot his job Apply tot his job