real estate capstone project github

10 Real Estate Data Science Projects

In the KPMG Global PropTech Survey 2018 , 49% of respondents identified artificial intelligence, big data, and data analytics as the technologies expected to have the most significant long-term impact on the real estate industry.

Are you interested in participating in the data-driven development of the real estate industry? Do you want to discover patterns in the real estate market? Here are ten awesome real estate machine learning projects to get you started.

Doma: Property Risk Evaluator Take-Home

We would like you to use a Jupyter (python) notebook to work with a slice of this data. You’ll get a sense of the type of questions that we deal with at States Title, and we’ll get a sense of your data science approach.

How you can do it:

Write python code that allows you to stand up a nationwide title insurance company:

• It should read the files default_notices.csv , train_property_data.csv , and test_property_data.csv , described below.
• It should append a new column, risk, to the test_property_data.csv file, which represents your prediction of the overall title risk for the property. This column should behave in such a way that properties with lower risk are predicted to be more profitable than properties with higher risk.
• You are at complete freedom to set the method for measuring risk, and the column itself can contain any real-valued number that satisfies part.

Real Estate Machine Learning Project For House Price Prediction

Want to learn how to build and evaluate a model’s performance and predictive power using machine learning regression algorithms? Developing a house price prediction model is a great way to start.

There’s a ton of accessible housing data online, e.g., sites like Zillow and Airbnb, and these datasets are perfect for executing this type of project. Zillow’s free datasets are a popular choice; the Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.

The project consists of two phases: Developing a model and training the data, then applying different regression algorithms and testing for the best fit.

How you can do it: This tutorial by Victor Roman takes you through all the steps of collecting, cleaning, and exploring housing data, then developing a machine learning model and applying different regression algorithms, and evaluating the model’s performance.

A more straightforward approach can be building a linear regression model and using K-fold cross-validation to measure the model’s accuracy. VarunSonavni uses this method with Python to examine the Bengaluru House price dataset on Kaggle in this tutorial .

WanderJaunt: Rental Price Analysis Take-Home

Data on short-term rental prices and occupancy is very important to WanderJaunt. It helps inform us how competitors are pricing, which influences our own pricing strategy and helps us benchmark our own occupancy and revenue per available room against similar properties.

In addition, it provides key inputs to the decision of what locations and markets we enter and what types of properties can be the most profitable.

Questions to answer:

• What data would you exclude from analysis for being unreliable or potentially a block instead of an actual booking?
• What is a good approach to estimating occupancy and revenue per unit?
• Which month appears to be more profitable? April or May?
• How much more revenue do places with 3 bedrooms make vs. places with 2 bedrooms?
• What are any other interesting insights you may have found?

Real Estate Data Science Capstone Project

Real estate developers and investors have always sought to understand where to acquire property and when to trigger development. They look for places where the housing prices are low, and the facilities (shops, restaurants, parks, hotels, etc.) and social venues are nearby.

According to the latest report by the prestigious Mckinsey consulting firm , big data and data analytics is the way to analyze the ton of nontraditional valuables that affect house prices and quickly identify potential investment opportunities.

How you can do it: This real estate data science capstone project tutorial by Muhammad Taha Khan uses publicly available data from Wikipedia and Foursquare API to develop a machine learning model that can cluster the data mentioned above visually for the large city of London.

The model uses an unsupervised learning K-means algorithm to cluster the boroughs and folium Python library to visualize and display the resulting clusters.The project includes housing data sets, and you can also check the code in its GitHub repository .

House Price Forecasting Using Zillow Economics Dataset

Clients, real estate agents, home trading firms, and other investors often have biased assumptions about whether home values ​​in a particular area will rise or fall. The recent UK and Australian-based studies suggest valuations between two professionals can differ by up to 40% .

So instead of making potentially biased or inaccurate assumptions, it’s better to use statistical methods to predict the value of homes over time.

The latest application combining an extensive database of traditional and nontraditional data, was used to forecast the three-year rent per square foot for multifamily buildings in Seattle. These machine-learning models predicted rents with an accuracy rate that exceeded 90 percent .

How you can do it: Follow Uma Gajendragadkar’s tutorial Using the Zillow Economic Dataset and Time Series Modeling with ARIMA to see how this project performs.

Identifying Real Estate Opportunities Using Machine Learning

In 2018, Skyline AI, a NewYork-based commercial real estate investment startup that uses machine learning algorithms to identify possible investment opportunities, acquired two multifamily residential complexes in Philadelphia for $26 million.

According to their PR release, they claim that they closed the deal with a price that was 12% under its expected value. “We saw that similar assets that had already been renovated were able to increase their rents by about $300 per unit,” Skyline AI CEO Guy Zipori .

Such a remarkable performance convinced lots of real estate investors that maybe they should be increasingly relying on machine learning. But developing machine learning algorithms that can accurately identify these opportunities is not easy, as the variables that affect pricing are not always easy to recognize.

How you can do it: This project develops a property price classification model using a current decade dataset from publicly available data from the Volusia County, Florida, Real Estate Appraisers website.

Algorithms utilize powerful machine learning, namely logistic regression, random forest, voting classifier, and XGBoost. The developed model can help real estate investors, mortgage lenders, and financial institutions make informed decisions.

You can use the study by Alejandro Baldominos to learn more about accomplishing such a daunting task. Published Public case studies are available at Cornell for more in-depth analysis.

Exploratory Data Analysis Of House Prices

Exploratory data analysis is a core skill for any aspiring data scientist. Learning how to explore and analyze data is a necessary process not only for training a particular model but also for various other purposes.

Advantages of performing an EDA:

• Significantly improves one’s understanding of the dataset.
• It helps to identify distribution, unique characteristics, or patterns in the dataset.
• It enables one to find outliers, duplicates, or null values.
• It represents the data visually in a more understandable manner.

How you can do it: This project uses a house prices dataset from Kaggle to perform such analysis in a simple and easy-to-understand way. You can also complete your research using this weekly updated USA housing dataset .

California Housing Price Prediction Machine Learning Project

Experimenting with accurate data is always the best way to learn about the fundamental challenges you face in the workplace. In this real-data project tutorial , Gurupratap S Matharu goes through an end-to-end real estate machine learning project to predict house prices in California using advanced regression.

How You Can Do It: The tutorial covers all the steps from understanding the business goals and acquiring the dataset, processing the data and experimenting with different ML models to find the best fit, and finally launching, monitoring, and maintaining the system.

If you like it, you can try to recreate the same project using different housing datasets from Kaggle.

Predicting Crimes And Creating A Safety District Index

Living in a safe community is something everyone is actively seeking. The Seattle Open Data Project provides access to the Seattle City Police Department’s 911 emergency response as a part of its open data project.

Using this data, you can cluster and map different types of crime and organize them by severity. Then overlay them on a population density-based crime density map to construct a model that predicts crimes and groups regions based on a safety index.

The cleaned dataset and code used to build this project are available in Jay Feng’s GitHub repository , and you can follow his blog post for more details on how to perform this type of analysis.

BONUS: House Prices – Advanced Regression Techniques Competition

Kaggle – the well-known data science community – is running an ongoing competition for data science students who have completed an online machine learning course and want to expand their skills before trying out featured competitions.

The competition is an excellent opportunity to build and test all data scientist skills. The competition has a clear goal, a public leaderboard, and numerous housing datasets and tutorials.

More Project Ideas from Interview Query

If you want more projects to develop your skills further, try our new Takehomes , where you solve more prolonged problems step-by-step with notebooks from different companies.

Takehomes will help you build your data science skills, including Python, SQL, and machine learning, and try out projects used in high-profile companies.

Additionally, you can look at other data science project lists and datasets from Interview Query:

• Top 10 Regression Datasets and Projects
• 31 Free Datasets for Your Next Project
• 12 Machine Learning Projects (Beginner to Advanced)
• 10 Python Projects with Source Code
• 21 Data Analytics Project Ideas and Datasets
• Top 12 Classification Machine Learning Projects

• Suggest edit

Welcome to C964!

Nothing on this website is an official WGU resource developed by the WGU product development team. See your COS page to review the official resources.

🚧 This site is under construction! Apologies in advance for typos, broken links, etc. Feel free to make recommendations and become a contributor! 👷🏽‍♀️

On 9/25/2023, C964 was updated to a new version, (SIM3). Though the rubric and task directions were reworded, the actual requirements and their assessment criteria are unchanged. The guidance and templates on this website apply to both versions.

Welcome to C964! #

Welcome! For the Computer Science capstone project, you’ll develop and present a machine learning application solving a proposed problem. The problem, the solution, and the presentation as a final product are up to you! The capstone allows you to demonstrate the application of skills collected throughout the CS program. Most importantly, that crucial skill setting CS majors apart, learning and applying new things. You are a problem-solver; this is your opportunity to shine.

The capstone includes three parts:

Task 1: Get course instructor topic approval -a preliminary step to ensure you starts in the right direction.

Task 2 part C: The “app.” Develop a working application of machine learning (ML).

Task 2 parts D, A, & B: Documentation communicating your product’s value and development process to audiences of varying technical understanding.

Choose a topic and get approval.

Task 1 details

Task 2 part C

Develop a working application of machine learning (ML).

Task 2 part C details

Task 2 parts A, B, & D

Present your product to audiences of varying technical understanding through documentation and visualizations.

Task 3 details

Start Here #

First, understand the project’s requirements. What they are -and what they aren’t. Watch the following video:

And review the What does the application need to do? section and part C of the Task 2 template .

Like the C950 -Data Structures and Algorithms II task and C951 -Intro to AI tasks 1 and 2, this project consists of a working application, ( Task 2 part C ), and accompanying documentation, Task 2 parts A, B, & D . But because of the breadth of allowable topics, we want to ensure you start working in the right direction, and thus require all topics to be approved by your assigned course instructor, Task 1 . So your next step is choosing a topic and having it approved.

John N. Williams

Adam Dobrinich

Finished Papers

Navigation Menu

Search code, repositories, users, issues, pull requests..., provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications

Real Estate Capstone Project

ksmdbhat/Real-Estate---Capstone-Project

Folders and files, repository files navigation, real-estate---capstone-project.

Real Estate Capstone Project Real Estate Real-Estate---Capstone-Project Description • A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. • The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate. • A statistical model needs to be created to predict the potential demand in dollars amount of loan for each of the region in the USA. Also, there is a need to create a dashboard which would refresh periodically post data retrieval from the agencies. • The dashboard must demonstrate relationships and trends for the key metrics as follows: number of loans, average rental income, monthly mortgage and owner’s cost, family income vs mortgage cost comparison across different regions. The metrics described here do not limit the dashboard to these few.

Dataset Description Variables Description • Second mortgage • Households with a second mortgage statistics • Home equity • Households with a home equity loan statistics • Debt • Households with any type of debt statistics • Mortgage Costs • Statistics regarding mortgage payments, home equity loans, utilities, and property taxes • Home Owner Costs • Sum of utilities, and property taxes statistics • Gross Rent • Contract rent plus the estimated average monthly cost of utility features • High school Graduation • High school graduation statistics • Population Demographics • Population demographics statistics • Age Demographics • Age demographic statistics • Household Income • Total income of people residing in the household • Family Income • Total income of people related to the householder

Project Task: Week 1 Data Import and Preparation:

• Import data.
• Figure out the primary key and look for the requirement of indexing.
• Gauge the fill rate of the variables and devise plans for missing value treatment. Please explain explicitly the reason for the treatment chosen for each variable. Exploratory Data Analysis (EDA):
• Perform debt analysis. You may take the following steps: • Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map. You may keep the upper limit for the percent of households with a second mortgage to 50 percent • Use the following bad debt equation: • Bad Debt = P (Second Mortgage ∩ Home Equity Loan) • Bad Debt = second_mortgage + home_equity - home_equity_second_mortgage • Create pie charts to show overall debt and bad debt • Create Box and whisker plot and analyze the distribution for 2nd mortgage, home equity, good debt, and bad debt for different cities • Create a collated income distribution chart for family income, house hold income, and remaining income
• Perform EDA and come out with insights into population density and age. You may have to derive new fields (make sure to weight averages for accurate measurements): • Use pop and ALand variables to create a new field called population density • Use male_age_median, female_age_median, male_pop, and female_pop to create a new field called median age • Visualize the findings using appropriate chart type
• Create bins for population into a new variable by selecting appropriate class interval so that the number of categories don’t exceed 5 for the ease of analysis. • Analyze the married, separated, and divorced population for these population brackets • Visualize using appropriate chart type
• Please detail your observations for rent as a percentage of income at an overall level, and for different states.
• Perform correlation analysis for all the relevant variables by creating a heatmap. Describe your findings.

Project Task: Week 2 Data Pre-processing:

• The economic multivariate data has a significant number of measured variables. The goal is to find where the measured variables depend on a number of smaller unobserved common factors or latent variables.
• Each variable is assumed to be dependent upon a linear combination of the common factors, and the coefficients are known as loadings. Each measured variable also includes a component due to independent random variability, known as “specific variance” because it is specific to one variable. Obtain the common factors and then plot the loadings. Use factor analysis to find latent variables in our dataset and gain insight into the linear relationships in the data. Following are the list of latent variables: • Highschool graduation rates • Median population age • Second mortgage statistics • Percent own • Bad debt expense

Data Modeling : 3. Build a linear Regression model to predict the total monthly expenditure for home mortgages loan. Please refer deplotment_RE.xlsx. Column hc_mortgage_mean is predicted variable. This is the mean monthly mortgage and owner costs of specified geographical location. Note: Exclude loans from prediction model which have NaN (Not a Number) values for hc_mortgage_mean. a) Run a model at a Nation level. If the accuracy levels and R square are not satisfactory proceed to below step. b) Run another model at State level. There are 52 states in USA. c) Keep below considerations while building a linear regression model: • Variables should have significant impact on predicting Monthly mortgage and owner costs • Utilize all predictor variable to start with initial hypothesis • R square of 60 percent and above should be achieved • Ensure Multi-collinearity does not exist in dependent variables • Test if predicted variable is normally distributed

Data Reporting: 4. Create a dashboard in tableau by choosing appropriate chart types and metrics useful for the business. The dashboard must entail the following: • Box plot of distribution of average rent by type of place (village, urban, town, etc.). • Pie charts to show overall debt and bad debt. • Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map. • Heat map for correlation matrix. • Pie chart to show the population distribution across different types of places (village, urban, town etc.).

• Jupyter Notebook 100.0%

2 Dauphin County real estate firms merge

• Updated: May. 04, 2024, 1:06 p.m. |
• Published: May. 02, 2024, 12:39 p.m.

Capstone Commercial has opened at 205 W. Caracas Ave. (Photo provided) Provided

• Daniel Urie | [email protected]

Derry Township-based Capstone Commercial has merged with another real estate company.

Lower Paxton Township-based Omni Realty Group merged into Capstone Commercial on March 25. The company was founded in 1998 by Mike Kushner. Omni Realty Group is a buyer/tenant representation agency.

“This strategic move marks a significant milestone in Capstone’s growth and ongoing commitment to providing comprehensive services to its clients through every aspect of the commercial real estate cycle,” the company said in a news release.

Stories by Daniel Urie

• Pa. nonprofit could potentially close 3 locations, laying off more than 100 people after losing state contract
• Down to 4: Another business closes at the Harrisburg Mall
• Central Pa. market welcomes 11 new vendors
• Dauphin County retailer reopens, gears up for ‘Free Comic Book Day’
• Mall staple closing all of its stores including these 21 Pa. locations

Kushner has joined Capstone as senior advisor and will also retain his title of buyer/tenant representation specialist.

“I am fortunate to have the opportunity to join the Capstone Commercial team. And I look forward to helping the company expand into new markets while continuing to deliver a high level of customer service to our clients,” Kushner said in a news release.

Terms of the agreement weren’t disclosed.

Capstone provides a variety of services including property acquisition, leasing services, investment analysis, business brokerage, market research, and corporate representation.

Capstone opened for business last summer and opened its Derry Township office in November.

The firm was founded by Ida McMurray, Naomi Brown, Matt Hoover and Dylan Kelly.

If you purchase a product or register for an account through a link on our site, we may receive compensation. By using this site, you consent to our User Agreement and agree that your clicks, interactions, and personal information may be collected, recorded, and/or stored by us and social media and other third-party partners in accordance with our Privacy Policy.

• Popular Professionals
• Design & Planning
• Construction & Renovation
• Finishes & Fixtures
• Landscaping & Outdoor
• Systems & Appliances
• Interior Designers & Decorators
• Architects & Building Designers
• Design-Build Firms
• Kitchen & Bathroom Designers
• General Contractors
• Kitchen & Bathroom Remodelers
• Home Builders
• Roofing & Gutters
• Cabinets & Cabinetry
• Tile & Stone
• Hardwood Flooring Dealers
• Landscape Contractors
• Landscape Architects & Landscape Designers
• Home Stagers
• Swimming Pool Builders
• Lighting Designers and Suppliers
• 3D Rendering
• Sustainable Design
• Basement Design
• Architectural Design
• Universal Design
• Energy-Efficient Homes
• Multigenerational Homes
• House Plans
• Home Remodeling
• Home Additions
• Green Building
• Garage Building
• New Home Construction
• Basement Remodeling
• Stair & Railing Contractors
• Cabinetry & Cabinet Makers
• Roofing & Gutter Contractors
• Window Contractors
• Exterior & Siding Contractors
• Carpet Contractors
• Carpet Installation
• Flooring Contractors
• Wood Floor Refinishing
• Tile Installation
• Custom Countertops
• Quartz Countertops
• Cabinet Refinishing
• Custom Bathroom Vanities
• Finish Carpentry
• Cabinet Repair
• Custom Windows
• Window Treatment Services
• Window Repair
• Fireplace Contractors
• Paint & Wall Covering Dealers
• Door Contractors
• Glass & Shower Door Contractors
• Landscape Construction
• Land Clearing
• Garden & Landscape Supplies
• Deck & Patio Builders
• Deck Repair
• Patio Design
• Stone, Pavers, & Concrete
• Paver Installation
• Driveway & Paving Contractors
• Driveway Repair
• Asphalt Paving
• Garage Door Repair
• Fence Contractors
• Fence Installation
• Gate Repair
• Pergola Construction
• Spa & Pool Maintenance
• Swimming Pool Contractors
• Hot Tub Installation
• HVAC Contractors
• Electricians
• Appliance Services
• Solar Energy Contractors
• Outdoor Lighting Installation
• Landscape Lighting Installation
• Outdoor Lighting & Audio/Visual Specialists
• Home Theater & Home Automation Services
• Handyman Services
• Closet Designers
• Professional Organizers
• Furniture & Accessories Retailers
• Furniture Repair & Upholstery Services
• Specialty Contractors
• Color Consulting
• Wine Cellar Designers & Builders
• Home Inspection
• Custom Artists
• Columbus, OH Painters
• New York City, NY Landscapers
• San Diego, CA Bathroom Remodelers
• Minneapolis, MN Architects
• Portland, OR Tile Installers
• Kansas City, MO Flooring Contractors
• Denver, CO Countertop Installers
• San Francisco, CA New Home Builders
• Rugs & Decor
• Home Improvement
• Kitchen & Tabletop
• Bathroom Vanities
• Bathroom Vanity Lighting
• Bathroom Mirrors
• Bathroom Fixtures
• Nightstands & Bedside Tables
• Kitchen & Dining
• Bar Stools & Counter Stools
• Dining Chairs
• Dining Tables
• Buffets and Sideboards
• Kitchen Fixtures
• Wall Mirrors
• Living Room
• Armchairs & Accent Chairs
• Coffee & Accent Tables
• Sofas & Sectionals
• Media Storage
• Patio & Outdoor Furniture
• Outdoor Lighting
• Ceiling Lighting
• Chandeliers
• Pendant Lighting
• Wall Sconces
• Desks & Hutches
• Office Chairs
• View All Products
• Designer Picks
• Side & End Tables
• Console Tables
• Living Room Sets
• Chaise Lounges
• Ottomans & Poufs
• Bedroom Furniture
• Nightstands
• Bedroom Sets
• Dining Room Sets
• Sideboards & Buffets
• File Cabinets
• Room Dividers
• Furniture Sale
• Trending in Furniture
• View All Furniture
• Bath Vanities
• Single Vanities
• Double Vanities
• Small Vanities
• Transitional Vanities
• Modern Vanities
• Houzz Curated Vanities
• Best Selling Vanities
• Bathroom Vanity Mirrors
• Medicine Cabinets
• Bathroom Faucets
• Bathroom Sinks
• Shower Doors
• Showerheads & Body Sprays
• Bathroom Accessories
• Bathroom Storage
• Trending in Bath
• View All Bath
• Houzz x Jennifer Kizzee
• Houzz x Motivo Home
• How to Choose a Bathroom Vanity

• Patio Furniture
• Outdoor Dining Furniture
• Outdoor Lounge Furniture
• Outdoor Chairs
• Adirondack Chairs
• Outdoor Bar Furniture
• Outdoor Benches
• Wall Lights & Sconces
• Outdoor Flush-Mounts
• Landscape Lighting
• Outdoor Flood & Spot Lights
• Outdoor Decor
• Outdoor Rugs
• Outdoor Cushions & Pillows
• Patio Umbrellas
• Lawn & Garden
• Garden Statues & Yard Art
• Planters & Pots
• Outdoor Sale
• Trending in Outdoor
• View All Outdoor
• 8 x 10 Rugs
• 9 x 12 Rugs
• Hall & Stair Runners
• Home Decor & Accents
• Pillows & Throws
• Decorative Storage
• Faux Florals
• Wall Panels
• Window Treatments
• Curtain Rods
• Blackout Curtains
• Blinds & Shades
• Rugs & Decor Sale
• Trending in Rugs & Decor
• View All Rugs & Decor
• Pendant Lights
• Flush-Mounts
• Ceiling Fans
• Track Lighting
• Wall Lighting
• Swing Arm Wall Lights
• Display Lighting
• Table Lamps
• Floor Lamps
• Lamp Shades
• Lighting Sale
• Trending in Lighting
• View All Lighting
• Bathroom Remodel
• Kitchen Remodel
• Kitchen Faucets
• Kitchen Sinks
• Major Kitchen Appliances
• Cabinet Hardware
• Backsplash Tile
• Mosaic Tile
• Wall & Floor Tile
• Accent, Trim & Border Tile
• Whole House Remodel
• Heating & Cooling
• Building Materials
• Front Doors
• Interior Doors
• Home Improvement Sale
• Trending in Home Improvement
• View All Home Improvement
• Cups & Glassware
• Kitchen & Table Linens
• Kitchen Storage and Org
• Kitchen Islands & Carts
• Food Containers & Canisters
• Pantry & Cabinet Organizers
• Kitchen Appliances
• Gas & Electric Ranges
• Range Hoods & Vents
• Beer & Wine Refrigerators
• Small Kitchen Appliances
• Cookware & Bakeware
• Tools & Gadgets
• Kitchen & Tabletop Sale
• Trending in Kitchen & Tabletop
• View All Kitchen & Tabletop
• Storage & Organization
• Baby & Kids
• Housekeeping & Laundry
• Pet Supplies

• View all photos
• Dining Room
• Breakfast Nook
• Family Room
• Bed & Bath
• Powder Room
• Storage & Closet
• Outdoor Kitchen
• Bar & Wine
• Wine Cellar
• Home Office
• Popular Design Ideas
• Kitchen Backsplash
• Deck Railing
• Privacy Fence
• Small Closet
• Stories and Guides
• Popular Stories
• Renovation Cost Guides
• Fence Installation Cost Guide
• Window Installation Cost Guide
• Discussions
• Design Dilemmas
• Before & After
• Houzz Research
• View all pros
• View all services
• View all products
• View all sales
• Living Room Chairs
• Dining Room Furniture
• Coffee Tables
• Home Office Furniture
• Join as a Pro
• Interior Design Software
• Project Management
• Custom Website
• Lead Generation
• Invoicing & Billing
• Landscape Contractor Software
• General Contractor Software
• Remodeler Software
• Builder Software
• Roofer Software
• Architect Software
• Takeoff Software
• Lumber & Framing Takeoffs
• Steel Takeoffs
• Concrete Takeoffs
• Drywall Takeoffs
• Insulation Takeoffs
• Stories & Guides
• LATEST FROM HOUZZ
• HOUZZ DISCUSSIONS
• SHOP KITCHEN & DINING
• Kitchen & Dining Furniture
• Sinks & Faucets
• Kitchen Cabinets & Storage
• Knobs & Pulls
• Kitchen Knives
• KITCHEN PHOTOS
• FIND KITCHEN PROS
• Bath Accessories
• Bath Linens
• BATH PHOTOS
• FIND BATH PROS
• SHOP BEDROOM
• Beds & Headboards
• Bedroom Decor
• Closet Storage
• Bedroom Vanities
• BEDROOM PHOTOS
• Kids' Room
• FIND DESIGN PROS
• SHOP LIVING
• Fireplaces & Accessories
• LIVING PHOTOS
• SHOP OUTDOOR
• Pool & Spa
• Backyard Play
• OUTDOOR PHOTOS
• FIND LANDSCAPING PROS
• SHOP LIGHTING
• Bathroom & Vanity
• Flush Mounts
• Kitchen & Cabinet
• Outdoor Wall Lights
• Outdoor Hanging Lights
• Kids' Lighting
• Decorative Accents
• Artificial Flowers & Plants
• Decorative Objects
• Screens & Room Dividers
• Wall Shelves
• About Houzz
• Houzz Credit Cards
• Privacy & Notice
• Cookie Policy
• Your Privacy Choices
• Mobile Apps
• Copyright & Trademark
• For Professionals
• Houzz vs. Houzz Pro
• Houzz Pro vs. Ivy
• Houzz Pro Advertising Reviews
• Houzz Pro 3D Floor Planner Reviews
• Trade Program
• Buttons & Badges
• Your Orders
• Shipping & Delivery
• Return Policy
• Houzz Canada
• Review Professionals
• Suggested Professionals
• Accessibility
• Houzz Support
• COUNTRY COUNTRY

Home & House Stagers in Elektrostal'

Location (1).

• Use My Current Location

Popular Locations

• Albuquerque
• Cedar Rapids
• Grand Rapids
• Indianapolis
• Jacksonville
• Kansas City
• Little Rock
• Los Angeles
• Minneapolis
• New Orleans
• Oklahoma City
• Orange County
• Philadelphia
• Portland Maine
• Salt Lake City
• San Francisco
• San Luis Obispo
• Santa Barbara
• Washington D.C.
• Elektrostal', Moscow Oblast, Russia

Professional Category (1)

• Accessory Dwelling Units (ADU)

Featured Reviews for Home & House Stagers in Elektrostal'

• Reach out to the pro(s) you want, then share your vision to get the ball rolling.
• Request and compare quotes, then hire the Home Stager that perfectly fits your project and budget limits.

A home stager is a professional who prepares a house for sale, aiming to attract more buyers and potentially secure a higher selling price. They achieve this through the following techniques:

• Rearranging furniture to optimize space and functionality.
• Decluttering to create a clean and spacious look.
• Making repairs to address visible issues.
• Enhancing aesthetics with artwork, accessories, and lighting.
• Introducing new furnishings to update the style.

Their goal is to present the house in the best light. Home stagers in Elektrostal' help buyers envision themselves living there, increasing the chances of a successful sale.

• Decluttering
• Furniture Selection
• Space Planning
• Art Selection
• Accessory Selection

Benefits of the home staging in Elektrostal':

• Attractive and inviting: Staging creates a welcoming atmosphere for potential buyers.
• Faster sale: Homes sell more quickly, reducing time on the market.
• Higher sale price: Staging can lead to higher offers and appeal to a wider range of buyers.
• Showcasing best features: Strategic arrangement highlights positives and minimizes flaws.
• Stand out online: Staged homes capture attention in online listings.
• Emotional connection: Staging creates a positive impression that resonates with buyers.
• Easy visualization: Buyers can easily picture themselves living in a staged home.
• Competitive advantage: Staging sets your home apart from others on the market.
• Affordable investment: Cost-effective way to maximize selling potential and ROI.
• Professional expertise: Experienced stagers ensure optimal presentation for attracting buyers.

What does an Elektrostal' home stager do?

What should i consider before hiring an interior staging company, questions to ask potential real estate staging companies in elektrostal', moscow oblast, russia:, business services, connect with us.