Build a Craigslist Rental and Housing Price Monitor (2026)

Thirdwatch's Craigslist Scraper pulls apartment and housing listings from any Craigslist city — title, price, location, neighborhood, full description, and posting date. Schedule daily pulls to build a rental price monitor that tracks trends by neighborhood, detects price drops, and alerts you to below-market deals. Built for real estate analysts, property managers, and rental market researchers.

Why monitor Craigslist housing prices

Craigslist is the largest peer-to-peer rental marketplace in the United States. Unlike Zillow or Apartments.com, which aggregate professional property managers, Craigslist captures individual landlords, small building owners, and sublets — the long tail of the rental market that institutional platforms miss. According to the Joint Center for Housing Studies at Harvard, individual landlords own roughly 40 percent of all rental units in the US, and Craigslist remains their primary listing channel.

For a rental market researcher, this means Craigslist reflects actual asking prices from a segment that aggregator sites undercount. A property manager benchmarking their units against local competition needs this data. A real estate investor scouting a new metro wants the ground-truth price distribution, not an algorithm's estimate. A relocation service advising corporate clients needs current neighborhood-level pricing. All of these reduce to the same pipeline: pull Craigslist apartment listings by city and neighborhood on a recurring schedule, store the time series, and compute rolling medians.

How does this compare to the alternatives?

Three ways to track Craigslist rental prices systematically:

Zillow and Apartments.com APIs are alternatives for institutional listings but miss the individual-landlord segment entirely. The Craigslist Scraper covers the peer-to-peer market that those platforms structurally cannot reach.

How to build a Craigslist rental price monitor in 4 steps

Step 1: How do I set up the Apify token and actor?

Create a free account at apify.com, go to Settings, then Integrations, and copy your API token:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull apartment listings for a specific city?

Use the city parameter with a Craigslist subdomain and category set to apa for apartments. Add a query to narrow by bedroom count or features.

import os, requests, pandas as pd

ACTOR = "thirdwatch~craigslist-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "city": "sfbay",
        "category": "apa",
        "query": "1br",
        "maxResults": 200,
    },
    timeout=300,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} one-bedroom listings in SF Bay")
print(df[["title", "price", "neighborhood", "posted_date"]].head(10))

Category apa covers apartments and housing for rent. For rooms and shares, use roo. For real estate for sale, use rea. The category code appears in the Craigslist URL when browsing.

Step 3: How do I parse prices and compute neighborhood medians?

Craigslist prices arrive as formatted strings. Strip the currency symbol and convert to numeric before aggregating:

import re
from datetime import datetime

def parse_price(price_str):
    if not price_str:
        return None
    digits = re.sub(r"[^\d.]", "", price_str)
    return float(digits) if digits else None

df["price_numeric"] = df["price"].apply(parse_price)
df["scrape_date"] = datetime.now().strftime("%Y-%m-%d")

# Drop rows without price
priced = df[df["price_numeric"].notna()].copy()

# Neighborhood median rent
neighborhood_stats = (
    priced.groupby("neighborhood")["price_numeric"]
    .agg(["median", "count", "min", "max"])
    .sort_values("median", ascending=False)
)
print(neighborhood_stats.head(15))

Over time, append each daily scrape to a CSV or database table. The scrape_date column lets you compute rolling 7-day and 30-day medians per neighborhood to spot trends.

Step 4: How do I schedule daily pulls and detect price drops?

Set up an Apify schedule for a daily morning pull, then run a comparison against your historical data:

curl -X POST "https://api.apify.com/v2/schedules?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "craigslist-sfbay-apartments-daily",
    "cronExpression": "0 8 * * *",
    "timezone": "America/Los_Angeles",
    "isEnabled": true,
    "actions": [{
      "type": "RUN_ACTOR",
      "actorId": "thirdwatch~craigslist-scraper",
      "runInput": {
        "city": "sfbay",
        "category": "apa",
        "maxResults": 200
      }
    }]
  }'

In your downstream pipeline, compare today's neighborhood medians against the 30-day rolling average. Flag neighborhoods where the median dropped more than 10 percent — these are potential softening signals or seasonal dips worth investigating.

# Pseudo-code for alert logic
historical = pd.read_csv("craigslist_rental_history.csv")
rolling_30d = (
    historical[historical["scrape_date"] >= thirty_days_ago]
    .groupby("neighborhood")["price_numeric"]
    .median()
)
today_medians = priced.groupby("neighborhood")["price_numeric"].median()

drops = (today_medians - rolling_30d) / rolling_30d
alerts = drops[drops < -0.10]
print(f"Neighborhoods with >10% price drop: {list(alerts.index)}")

Sample output

A single record from the dataset for a San Francisco rental listing:

{
  "title": "Spacious 1BR in Mission District - Laundry In Unit",
  "price": "$2,850",
  "location": "San Francisco",
  "neighborhood": "Mission District",
  "description": "Bright and spacious one-bedroom apartment on Valencia Street. In-unit washer/dryer, dishwasher, hardwood floors throughout. One block from BART. No pets. Available July 1. First/last/deposit required.",
  "posted_date": "2026-05-22",
  "category": "apa",
  "url": "https://sfbay.craigslist.org/sfc/apa/d/san-francisco-spacious-1br-mission/7823456789.html"
}

price is the raw listing price as posted. neighborhood is self-reported by the landlord — "Mission District" and "The Mission" both appear and need normalization. description contains the full post body including amenities, move-in costs, and restrictions. posted_date in ISO format lets you sort by freshness.

Common pitfalls

Three issues surface in production rental monitors. According to the FTC's 2024 Consumer Sentinel report, rental scams ranked among the top 10 fraud categories with median losses exceeding $1,000 per victim. Spam and scam listings — Craigslist rental categories attract fake posts with below-market prices designed to collect deposits. Filter outliers below the 5th percentile of neighborhood price and flag listings with generic descriptions under 50 words. Neighborhood normalization — posters use inconsistent names. "SOMA", "SoMa", and "South of Market" all refer to the same area. Build a canonical-name lookup table for your target metros and fuzzy-match incoming neighborhood values. Price format variation — some listings use weekly or per-room pricing without marking it clearly. A "$800/mo" 4BR listing might actually be $800 per room. Cross-reference bedroom count from the description against the price to catch per-room pricing.

The actor handles Craigslist's pagination and HTML parsing, returning clean records. For multi-city monitors, run one Apify schedule per metro and merge into a single time-series table downstream.

Related use cases

• Scrape Craigslist listings for market research
• Find Craigslist jobs by city and category
• Track Craigslist used car prices for arbitrage
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• The complete guide to scraping real estate data
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