Remote Work Hub Market Analyzer

Compare cities for remote-work hub opportunity. This actor helps coworking operators, coliving operators, tourism boards, real estate developers, and distributed teams decide which destinations deserve deeper research, expansion planning, partnership outreach, or relocation review.

It returns one decision row per location with opportunityScore, marketStage, recommendedAction, benchmark comparison, coworking, density, internet, community, affordability, saturation, and coliving gap scores.

This is an analyzer, not a raw directory scraper. You provide target cities and a buyer profile; the actor resolves public sources internally and produces a buyer-facing market signal.

Why Use This Actor

Remote-work destinations are often evaluated through city guides, coworking directories, blog posts, cost pages, and subjective community chatter. That is slow to compare and easy to overfit to search results.

This actor turns the workflow into a repeatable benchmark:

Question	Output fields
Which city looks strongest for this buyer profile?	opportunityScore, relativePosition
Is the market already crowded?	osmCoworkingCount, coworkingSupplySignal, saturationRiskSignal
Is there a possible live-work or coliving gap?	colivingGapSignal, communityMomentumSignal
What should the buyer do next?	recommendedAction, marketStage
How does the city compare with alternatives?	benchmarkAgainst, betterThanBenchmarksOn, worseThanBenchmarksOn

Data Strategy

The actor uses stable public sources and internal scoring rules:

• Nominatim / OpenStreetMap to resolve city identity and bounding boxes.
• OpenStreetMap Overpass API to count features tagged office=coworking inside the resolved city area.
• Wikivoyage summary API for lightweight city context when available.
• Internal destination context for known remote-work areas, broad affordability pressure tiers, and fallback coworking counts for selected hubs when Overpass temporarily fails.

The actor does not use Numbeo. It also does not rely on Teleport, Nomad List, Coworker, Coliving.com, Expatistan, or Speedtest/Ookla as core sources.

Use Cases

• Coworking operators: compare expansion targets and spot underserved, competitive, or mature hubs.
• Coliving operators: identify cities where community and workspace signals suggest live-work demand.
• Tourism boards and DMOs: benchmark destinations before promoting remote-work programmes.
• Real estate developers: screen cities for flexible workspace or mixed-use concepts.
• Remote teams: compare hubs for meetups, relocation shortlists, or distributed office planning.

Input

The input is intentionally short. Users do not choose sources; source resolution is part of the actor.

Field	Type	Required	Description
locations	array	yes	Cities to analyze, ideally with country context.
buyerProfile	string	no	Decision lens: coworking_operator, coliving_operator, tourism_board, real_estate_developer, or remote_team.
benchmarkAgainst	array	no	Optional comparison cities. Leave blank to use the default benchmark set for the buyer profile.
datasetId	string	no	Existing Apify dataset to append rows to as well as the default run dataset.
runId	string	no	Optional external batch or run ID copied into output rows.

Example Input

{
  "locations": ["Lisbon, Portugal"],
  "buyerProfile": "coworking_operator",
  "benchmarkAgainst": ["Porto, Portugal"]
}

Output

Each dataset row is a decision record for one target location.

{
  "location": "Lisbon, Portugal",
  "city": "Lisbon",
  "country": "Portugal",
  "region": "Europe",
  "buyerProfile": "coworking_operator",
  "opportunityScore": 63,
  "marketStage": "competitive_growth_market",
  "recommendedAction": "Consider differentiated coworking, niche communities, or partnerships instead of a generic workspace launch.",
  "relativePosition": "above_benchmark_average",
  "benchmarkAgainst": ["Barcelona, Spain", "Medellin, Colombia", "Chiang Mai, Thailand"],
  "betterThanBenchmarksOn": ["workspace_density", "internet_readiness"],
  "worseThanBenchmarksOn": ["affordability_pressure"],
  "osmCoworkingCount": 37,
  "coworkingSupplyScore": 92,
  "coworkingSupplySignal": "strong",
  "workspaceDensityPerKm2": 0.08,
  "workspaceDensityScore": 71,
  "workspaceDensitySignal": "moderate",
  "internetQualityScore": 82,
  "internetReadinessSignal": "strong",
  "communityMomentumScore": 88,
  "communityMomentumSignal": "strong",
  "affordabilityPressure": "high",
  "affordabilityRiskScore": 78,
  "saturationRiskScore": 73,
  "saturationRiskSignal": "medium",
  "colivingGapScore": 58,
  "colivingGapSignal": "moderate",
  "exampleCoworkingSpaces": ["Heden", "Impact Hub Lisbon", "Second Home Lisboa"],
  "knownRemoteWorkAreas": ["Santos", "Cais do Sodre", "Principe Real", "Anjos", "Marvila"]
}

Key Fields

Field	Meaning
opportunityScore	Buyer-specific 0 to 100 opportunity score.
marketStage	Practical market tier, such as underserved_opportunity, competitive_growth_market, or mature_competitive_hub.
recommendedAction	Plain-English next action for the selected buyer profile.
relativePosition	Whether the location is above, below, or near the benchmark average.
betterThanBenchmarksOn	Signals where the city beats the benchmark average.
worseThanBenchmarksOn	Signals where the city lags the benchmark average.
osmCoworkingCount	Count of mapped OpenStreetMap coworking features, with internal fallback counts for selected known hubs when Overpass temporarily fails. This is a public map signal, not a complete market census.
coworkingSupplyScore	0 to 100 score for mapped coworking supply.
workspaceDensityPerKm2	Coworking map count divided by approximate bounding-box area.
workspaceDensityScore	0 to 100 score for mapped coworking density.
internetQualityScore	0 to 100 internet-readiness score for remote-work decisions. It is not a measured Mbps result.
communityMomentumScore	0 to 100 score for remote-work community momentum.
affordabilityPressure	Broad pressure tier from internal context. The actor does not return exact monthly costs.
affordabilityRiskScore	0 to 100 score where higher means more affordability pressure.
saturationRiskScore	0 to 100 score where higher means a more competitive or mature market.
colivingGapScore	0 to 100 score for possible live-work market gap.

API Usage

curl "https://api.apify.com/v2/acts/trovevault~remote-work-hub-market-analyzer/runs" \
  -X POST \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "locations": ["Lisbon, Portugal"],
    "buyerProfile": "coworking_operator",
    "benchmarkAgainst": ["Porto, Portugal"]
  }'

After the run finishes, fetch rows from the default dataset:

curl "https://api.apify.com/v2/datasets/DATASET_ID/items?clean=true&format=json" \
  -H "Authorization: Bearer $APIFY_TOKEN"

How Benchmarks Work

benchmarkAgainst is optional. It changes the output from "this city has a score" to "this city is stronger or weaker than the comparison set on specific business signals." If blank, the actor uses a default comparison set for the selected buyerProfile.

Limitations

• OpenStreetMap coverage varies by city. osmCoworkingCount is a mapped public signal, not a complete list of every coworking operator.
• For selected known hubs, the actor may use an internal fallback coworking count when Overpass returns a temporary error. The fallback is recorded in RUN_SUMMARY, not in public dataset rows.
• The actor does not scrape Numbeo and does not output exact cost-of-living estimates.
• Internet readiness is a tiered decision signal, not a measured Mbps speed-test result.
• Wikivoyage summaries may be missing or generic for some destinations.
• This actor is designed for screening and prioritization. High-stakes expansion, leasing, tourism-budget, or relocation decisions should still include manual due diligence.
• Source diagnostics are stored in RUN_SUMMARY so public dataset rows stay focused on the buyer decision.

FAQ

Does this actor scrape Nomad List, Teleport, Coworker, or Coliving.com?

No. Those sources are not used as core sources in this MVP. The actor is built around more stable public map and destination signals.

Does this actor use Numbeo?

No. Numbeo is intentionally excluded.

Why is the coworking count lower than a commercial directory?

The count comes from OpenStreetMap features tagged office=coworking. It is useful as a comparable public mapping signal, but it is not a full commercial inventory.

Can I schedule it?

Yes. Schedule the actor in Apify and keep the same benchmark set to monitor how relative signals change over time.

Can I append results to an existing dataset?

Yes. Pass datasetId and the actor will push rows to both the default run dataset and that dataset.

Changelog

0.1.0

• Pivoted from a broad digital nomad scraper to a B2B remote-work hub market opportunity analyzer.
• Removed source picker inputs.
• Removed Numbeo and other fragile/proprietary sources from the core workflow.
• Added benchmark comparison, buyer-profile weighting, market stage, and recommended action fields.