Housing Prices by Region

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

np.random.seed(555)

regions = ['Urban Core', 'Suburban', 'Exurban', 'Rural']
data = []
for region in regions:
    n = 42
    sqft = np.random.uniform(800, 4000, n)
    price_per_sqft = {'Urban Core': 450, 'Suburban': 280, 'Exurban': 180, 'Rural': 120}
    price = price_per_sqft[region] * sqft / 1000 + np.random.normal(0, 50, n)
    for sq, p in zip(sqft, price):
        data.append({'Square Feet': sq, 'Price ($K)': p, 'Region': region})

df = pd.DataFrame(data)

sns.set_style("whitegrid", {
    'axes.facecolor': '#ffffff',
    'figure.facecolor': '#ffffff',
    'grid.color': '#eeeeee'
})

palette = ['#C82909', '#27D3F5', '#6CF527', '#F5D327']

g = sns.lmplot(
    data=df,
    x='Square Feet',
    y='Price ($K)',
    hue='Region',
    col='Region',
    col_wrap=2,
    height=3.5,
    aspect=1.2,
    palette=palette,
    scatter_kws={'alpha': 0.7, 's': 50, 'edgecolor': 'white', 'linewidths': 0.6},
    line_kws={'linewidth': 2.5},
    ci=95
)

g.fig.set_facecolor('#ffffff')
for ax in g.axes.flat:
    ax.set_facecolor('#ffffff')
    for spine in ax.spines.values():
        spine.set_color('#dddddd')

g.fig.suptitle('Home Valuation by Location', fontsize=14, fontweight='bold', color='#1a1a1a', y=1.02)
plt.tight_layout()
plt.show()