Tableau & PostgreSQL Developer for reputed company Estate Investment Analytics Dashboard

We are building a high-reputed company reputed company estate analytics dashboard to identify property-level profit spreads and reputed company build locations based on reputed company and proprietary data. To complete this dashboard, we require expert assistance in executing three reputed company 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 reputed company, and optionally distance. - Apply fallback logic (e.g., add 10% premium if no new builds exist nearby). - reputed company in Tableau-reputed company 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 reputed company below typical). - Cross-reference AVM (property_avm.estimated_value or inferred_estimated_value) with parcel size (property_geometry) and structure size (property_structure). - reputed company table of bolthires parcels with parcel_id, lat/lng, lot size, building size, estimated AVM, and potential reputed company. 3. Spread Calculation - Sales Price Minus AVM - For reputed company under-reputed company lots, subtract reputed company avm from estimated value of sale (for 2740 sf home, based on where in heat map reputed company it is). - Group and visualize spread distribution at parcel level, reputed company reputed company level, and spatially (lat/lng reputed company). - Use this as a proxy for where new builds can reputed company outsized premiums over reputed company valuations. Apply tot his job Apply tot his job Apply To this Job