Open House Visitor Feedback Dataset

This dataset provides detailed feedback from visitors at real estate open house events, including quantitative ratings of agents and properties, qualitative comments, and follow-up preferences. It enables comprehensive evaluation of agent performance and property appeal, supporting data-driven improvements in real estate marketing and customer experience.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Agent performance analytics and benchmarking

Sample rows

preview · 8 of 35 rows · all 18 columns
feedback_idstringfollow_up_methodstringproperty_condition_ratingintegerwould_recommend_agentbooleanevent_idstringproperty_idstringagent_idstringvisitor_idstringvisit_datetimedatetimefeedback_datetimedatetimeagent_professionalism_ratingintegeragent_knowledge_ratingintegeroverall_experience_ratingintegerwould_consider_propertybooleanmost_liked_featurestringleast_liked_featurestringadditional_commentsstringcontact_requestedboolean
FBK-0001email4trueEVT-20230601APRP-1021AGT-501VST-20012023-06-01T11:15:002023-06-01T14:22:00555trueSpacious kitchen with modern appliancesLimited parking availableAgent was extremely helpful and friendly.true
FBK-0002none3trueEVT-20230601APRP-1021AGT-501VST-20022023-06-01T12:30:002023-06-01T13:02:00444falseLarge backyard and patioOutdated bathroomsAppreciated the refreshments provided.false
FBK-0003phone4trueEVT-20230601APRP-1021AGT-501VST-20032023-06-01T14:00:002023-06-01T15:21:00454trueBright living room with natural lightNo swimming poolWould love to see more properties like this.true
FBK-0004none3falseEVT-20230601BPRP-1022AGT-502VST-20042023-06-01T10:45:002023-06-01T11:10:00333falseQuiet neighborhoodSmall bedroomsAgent seemed rushed.false
FBK-0005email5trueEVT-20230601BPRP-1022AGT-502VST-20052023-06-01T11:30:002023-06-01T12:50:00555trueModern finishes throughoutN/AFantastic overall experience.true
FBK-0006none2falseEVT-20230601BPRP-1022AGT-502VST-20062023-06-01T13:15:002023-06-01T13:16:00222falseClose to public transportProperty needs repairsFelt the price was too high.false
FBK-0007phone5trueEVT-20230602APRP-1023AGT-503VST-20072023-06-02T10:00:002023-06-02T10:20:00545trueOpen floor planBusy street nearbyWould recommend to friends.true
FBK-0008none4trueEVT-20230602APRP-1023AGT-503VST-20082023-06-02T11:12:002023-06-02T11:45:00444falseLarge master bedroomNo garageAgent answered all my questions.false

What the 35 rows show

from the 35-row sample

None (follow up method) stands out: mean property_condition_rating is 3.0, against 4.8 for the rest.

  • 63%would_recommend_agent = true
  • 4median property_condition_rating
  • 4median agent_professionalism_rating
  • 4median agent_knowledge_rating
  • 4median overall_experience_rating
Mean property_condition_rating by follow_up_method35 rows
0484.8phone6 rows4.7email7 rows3.0none22 rows
property_condition_rating35 rows, in bands of 0.5
05106010091023.55property_condition_rating →

Median 4, from 2 to 5.

event_id35 rows · top 10 of 12 values
  1. EVT-20230601A3
  2. EVT-20230601B3
  3. EVT-20230602A3
  4. EVT-20230602B3
  5. EVT-20230603A3
  6. EVT-20230603B3
  7. EVT-20230604A3
  8. EVT-20230604B3
  9. EVT-20230605A3
  10. EVT-20230605B3
18 columns by typefrom the column list below
  • string 9
  • integer 4
  • datetime 2
  • boolean 3

Columns

18 columns in four groups
blueprint · 18 columns
columntypedescriptionexample
Text 9 columns
feedback_idstringUnique identifier for each visitor feedback entry.uniqueFBK-0001
event_idstringUnique identifier for the open house event.12 eventsEVT-20230601A
property_idstringUnique identifier for the property being shown.12 propertiesPRP-1021
agent_idstringUnique identifier for the real estate agent hosting the open house.12 agentsAGT-501
visitor_idstringUnique identifier for the visitor providing feedback (can be anonymized).VST-2001
most_liked_featurestringShort text describing what the visitor liked most about the property or event.up to 255 · optionalLarge backyard and patio
least_liked_featurestringShort text describing what the visitor liked least about the property or event.up to 255 · optionalOutdated bathrooms
additional_commentsstringAdditional qualitative feedback or comments from the visitor.optionalAgent seemed rushed.
follow_up_methodstringPreferred follow-up method if contact was requested (e.g., phone, email, none).phone · email · none · optionalemail
Numbers 4 columns
agent_professionalism_ratingintegerVisitor's rating of the agent's professionalism (1-5 scale).1 to 55
agent_knowledge_ratingintegerVisitor's rating of the agent's knowledge about the property (1-5 scale).1 to 55
property_condition_ratingintegerVisitor's rating of the property's condition (1-5 scale).1 to 54
overall_experience_ratingintegerVisitor's overall rating of the open house experience (1-5 scale).1 to 55
Dates and times 2 columns
visit_datetimedatetimeDate and time when the visitor attended the open house.2023-06-01T11:15:00
feedback_datetimedatetimeDate and time when the feedback was submitted.2023-06-01T14:22:00
True or false 3 columns
would_recommend_agentbooleanIndicates if the visitor would recommend the agent to others.true
would_consider_propertybooleanIndicates if the visitor would consider buying or renting the property.true
contact_requestedbooleanIndicates if the visitor requested further contact from the agent.true

Use it for

  • would recommen…63%22 of 35 rowsmean property conditi…4.8phone4.7email3.0none

    A real estate dashboard

    The would_recommend_agent rate, property_condition_rating by follow_up_method and a breakdown of event_id. Excel, Power BI or Tableau.

  • Why do the 22 none rows have a mean property_condition_rating of 3.0?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable feedbacks with event_id, property_id and agent_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

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This dataset35 rows18 columns
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blueprint · open-house-visitor-feedback-dataset

Behind this dataset

Same schema. As many rows as you need.

These 35 rows came out of a blueprint — 18 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Log visitor feedback scores
  • Link feedback to property and agent
  • Flag negative comments for review
  • Summarize feedback by property type
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
open-house-visitor-feedback-dataset

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