Real Estate Markets Bubble

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)

fig, ax = plt.subplots(figsize=(14, 9), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

cities = ['San Francisco', 'NYC', 'Austin', 'Miami', 'Seattle', 'Denver', 'Phoenix', 'Nashville']
median_price = np.array([1350, 850, 580, 620, 780, 620, 450, 520])
price_sqft = np.array([1100, 1450, 350, 580, 620, 380, 280, 320])
inventory = np.array([1.8, 2.5, 2.2, 1.5, 1.9, 1.4, 3.2, 2.1])

colors = ['#F5276C', '#276CF5', '#6CF527', '#27D3F5', '#4927F5', '#F5B027', '#F54927', '#27F5B0']
sizes = inventory * 300

for glow_mult, glow_alpha in [(2.0, 0.03), (1.6, 0.05), (1.3, 0.08)]:
    ax.scatter(median_price, price_sqft, s=sizes*glow_mult, c='#000000', alpha=glow_alpha, edgecolors='none')

ax.scatter(median_price, price_sqft, s=sizes, c=colors, alpha=0.85, edgecolors='white', linewidth=2)
ax.scatter(median_price - np.sqrt(sizes)*0.8, price_sqft + np.sqrt(sizes)*1.2, s=sizes*0.2, c='white', alpha=0.6, edgecolors='none')

for i, city in enumerate(cities):
    offset_y = np.sqrt(sizes[i])/2 + 10
    ax.annotate(city, (median_price[i], price_sqft[i]), fontsize=9, color='#1f2937',
                ha='center', va='bottom', xytext=(0, offset_y), textcoords='offset points', fontweight='bold')

ax.text(0.0, 1.08, 'Real Estate Markets', transform=ax.transAxes, fontsize=24, color='#1f2937', fontweight='bold')
ax.text(0.0, 1.02, 'Median Price vs Price/Sqft · Bubble size = Inventory', transform=ax.transAxes, fontsize=11, color='#6b7280')

ax.set_xlabel('Median Price ($K)', fontsize=14, color='#4b5563', fontweight='500', labelpad=15)
ax.set_ylabel('Price per Sq Ft ($)', fontsize=14, color='#4b5563', fontweight='500', labelpad=15)
ax.tick_params(colors='#6b7280', labelsize=11, length=0)

for y in [0, 500, 1000, 1500]:
    ax.axhline(y=y, color='#f3f4f6', linewidth=1, zorder=0)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()
plt.show()