data-portfolio

King County, Washington — House Sales Dashboard

Visual analytics project to understand price drivers and neighborhood patterns for residential properties in King County, WA.

Title: King County House Sales Dashboard — Price Drivers & Market Patterns
Business problem: Identify how features (living area, bathrooms, grade/finish quality, view, condition, location) affect sale price, and turn those insights into actionable guidance for pricing, renovation priorities, and marketing strategy.
Dataset: HouseData.xlsx — 21,060 rows × 16 columns . Transactions 2014-05-02 to 2015-05-27 with fields such as price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, grade, yr_built, zipcode, lat, long, date.
Tools used: Tableau (dashboards), Microsoft Excel
Timeline: ~3–4 days total (data understanding/cleaning: 1–2; dashboarding/insights: 1–2; documentation: 0.5).

Key finding 1 — Technical price drivers Highest correlations with price: grade (0.665), sqft_living (0.656), bathrooms (0.486), lat (0.396). A simple regression gives slope ≈ $197 per sqft (≈ +$19.7k per additional 100 sqft of living area).
Key finding 2 — View & condition premium Avg price for Excellent view + Very Good condition ≈ $1.014M vs No View + Average condition ≈ $482k → ≈ $532k (~110%) premium. Implication: upgrades that improve finish grade/condition and perceived view meaningfully lift value.
Business impact
- Pricing: factor-based guidance (grade, living area, bathrooms, view/condition) supports listing price and negotiation.
- Renovation ROI: prioritize finish grade and bathroom upgrades for stronger value uplift.
- Targeting: spatial clusters (lat/zipcode) show higher averages → focus marketing on premium pockets.

Data size: 21,060 records; 2014-05-02 → 2015-05-27; ~70 zipcodes; no missing values on key fields.
Quick stats: mean price ≈ $500k; mean July 2014 ≈ $509k.
Correlations to price: grade (0.665), sqft_living (0.656), bathrooms (0.486), lat (0.396), bedrooms (0.317).
Simple regression: ~ $197 per sqft (price ~ sqft_living).
Heatmap takeaway: stronger view × condition combos consistently command higher prices.

Delivers a pricing framework grounded in physical and quality features → faster listing/appraisal decisions.
Identifies high-ROI renovations (finish grade, bathroom upgrades) and quantifies view premium.
Provides market mapping to direct campaigns toward higher-value areas.