This dataset summarizes findings from a 2025 link building survey of 518 SEO professionals, including backlink pricing, strategy trends, and industry opinions.

The following link building statistics explain why quality links are now more important than ever and what kinds of links you should be building.

During a 2024 survey conducted among search engine optimization (SEO) professionals worldwide, approximately ** percent of respondents identified superior content as the most effective link building strategy, utilizing it for promotion and encouraging linking. Alternative media strategies, including infographics, videos, and tools, ranked second, cited by ** percent of respondents.

Survey results collected from 300+ US-based marketing agencies on what they do to acquire backlinks for their clients and what kind of results they observe from those efforts

Dofollow links are the main links that every SEO wants. But 48% of marketers reported that nofollow are a part of their process as well.

The link building tools market is experiencing robust growth, driven by the increasing importance of search engine optimization (SEO) for businesses of all sizes. The market's value, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising adoption of digital marketing strategies, particularly among Small and Medium-sized Enterprises (SMEs), is creating significant demand for efficient and effective link building solutions. Secondly, the evolving landscape of search engine algorithms necessitates sophisticated tools capable of identifying high-quality backlinks and monitoring link profiles. Thirdly, the increasing availability of cloud-based link building tools offers greater accessibility and scalability, catering to diverse business needs and budgets. The market is segmented by application (SMEs and Large Enterprises) and type (cloud-based and on-premises), with cloud-based solutions currently dominating due to their flexibility and cost-effectiveness. While the North American market currently holds the largest share, regions like Asia-Pacific are showing rapid growth, fueled by expanding digital economies and increasing internet penetration. The competitive landscape is characterized by a mix of established players and emerging startups. Companies like Semrush, Ahrefs, and Moz hold significant market share, leveraging their comprehensive toolsets and brand recognition. However, specialized tools like BuzzStream and Linkody cater to specific needs and are gaining traction. The market faces some challenges, including the ongoing evolution of SEO best practices and the risk of penalties for manipulative link building techniques. Despite these restraints, the long-term outlook for the link building tools market remains positive, driven by continuous advancements in SEO technology and the sustained importance of organic search traffic for businesses. The increasing complexity of SEO and the growing need for data-driven strategies will further fuel the demand for sophisticated link building tools in the coming years.

The Link Building Tool market has become an essential facet of digital marketing and SEO strategies, providing businesses with the means to enhance their online visibility and authority. As companies increasingly pivot to digital platforms to engage with their audience, effective link building emerges as a crucial s

During a 2024 survey carried out among search engine optimization (SEO) professionals worldwide, ** percent of respondents stated that algorithm changes were one of the most difficult challenges SEO posed. Link building ranked second, named by ** percent of respondents.

During a 2023 survey carried out among search engine optimization (SEO) professionals worldwide, **** percent stated that link building was their most effective SEO strategy. Overall content strategy ranked first, named by **** percent of respondents.

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This dataset provides a detailed analysis of the SEO benefits and strategies for businesses participating in local business incubator programs in Colorado Springs. It covers the impact on local search rankings, local citations, link building, content collaboration, and other key SEO factors. The data was gathered through industry research, expert interviews, and case studies of businesses that have successfully leveraged incubator programs to boost their online visibility and organic traffic.

The BuildingsBench datasets consist of:

• Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock.
• 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF.

Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB).

BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below:

1. ElectricityLoadDiagrams20112014
2. Building Data Genome Project-2
3. Individual household electric power consumption (Sceaux)
4. Borealis
5. SMART
6. IDEAL
7. Low Carbon London

A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

Note: Starting on 12/10/2024 this dataset will only publish new records (rows) that are added and update any existing records that have updated information. New and updated records can be identified by using the data_as_of and data_loaded_at fields. To identify the most recent date when the dataset was last updated, use the maximum data_as_of or data_loaded_at value.

A. SUMMARY This dataset lists each review step on a building permit or site permit addenda routing list. Addenda are the more specific or detailed plans for construction that are submitted for review after a site permit has been issued. This dataset details the review timeline and departmental involvement for permits and addenda.

To better understand a routing list, we recommend looking up a permit number on DBI’s online Permit Tracking System and reviewing the table under “Addenda details” which lists all the departmental stations a permit is routed to for review. In this dataset, the same information from the online routing table are presented along with additional characteristics related to the associated permit or addenda.

A full data dictionary for this dataset is available by clicking here.

B. HOW THE DATASET IS CREATED This dataset was created by extracting routing information and associated permit/addenda characteristics from DBI’s Permit Tracking System.

C. UPDATE PROCESS The process that builds this dataset will run nightly and include all permits entered into the system up to the time of the refresh (see the “data as of” column in the dataset).

D. HOW TO USE THIS DATASET This dataset can be used in several ways, including 1) looking up a specific permit number, 2) viewing work done or in progress by specific department stations, 3) calculating review timelines in aggregate, etc. The Building Permits dataset can be combined or joined to this dataset to pull in additional characteristics from the full building permits or site permits (including filed/issued/completed dates, addresses, descriptions, etc.).

E. RELATED DATASETS Department of Building Inspections Permits Data Building Permits (Public) Building Permits (Unique) Department of Building Inspection Permit Addenda with Routing Building Permits Contacts Electrical Permits Plumbing Permits Boiler Permits Dwelling Unit Completion Counts by Building Permit

Planning Department Permits Planning Department Permits Public Works Construction Permits Department of Public Works Street Use Permits Department of Public Works Large Utility Excavation Permits

Fire Department Permits Fire Department Permits

Other housing/ construction related datasets related to building permits Planning Department Housing Development Pipeline Quarterly datasets

Mayor’s Office of Housing and Community Development: Affordable Housing Pipeline Affordable Housing Pipeline

For even more permit datasets, click here and search “Permits."

Commercial reference buildings provide complete descriptions for whole building energy analysis using EnergyPlus (see "About EnergyPlus" resource link) simulation software. Included here is data pertaining to the reference building type "Warehouse" for each of the 16 climate zones described on the Wiki page (see "OpenEI Wiki Page for Commercial Reference Buildings" resource link), and each of three construction categories: new (2004) construction, post-1980 construction existing buildings, and pre-1980 construction existing buildings. The dataset includes four key components: building summary, zone summary, location summary and a picture. Building summary includes details about: form, fabric, and HVAC. Zone summary includes details such as: area, volume, lighting, and occupants for all types of zones in the building. Location summary includes key building information as it pertains to each climate zone, including: fabric and HVAC details, utility costs, energy end use, and peak energy demand. In total, DOE developed 16 reference building types that represent approximately 70% of commercial buildings in the U.S.; for each type, building models are available for each of the three construction categories. The commercial reference buildings (formerly known as commercial building benchmark models) were developed by the U.S. Department of Energy (DOE), in conjunction with three of its national laboratories. Additional data is available directly from DOE's Energy Efficiency & Renewable Energy (EERE) website (see "About Commercial Buildings" resource link), including EnergyPlus software input files (.idf) and results of the EnergyPlus simulations (.html). Note: There have been many changes and improvements since this dataset was released. Several revisions have been made to the models and moved to a different approach to representing typical building energy consumption. For current data on building energy consumption please see the ComStock resource below.

Commercial reference buildings provide complete descriptions for whole building energy analysis using EnergyPlus (see "About EnergyPlus" resource link) simulation software. Included here is data pertaining to the reference building type "Small Hotel" for each of the 16 climate zones described on the Wiki page (see "OpenEI Wiki Page for Commercial Reference Buildings" resource link), and each of three construction categories: new (2004) construction, post-1980 construction existing buildings, and pre-1980 construction existing buildings. The dataset includes four key components: building summary, zone summary, location summary and a picture. Building summary includes details about: form, fabric, and HVAC. Zone summary includes details such as: area, volume, lighting, and occupants for all types of zones in the building. Location summary includes key building information as it pertains to each climate zone, including: fabric and HVAC details, utility costs, energy end use, and peak energy demand. In total, DOE developed 16 reference building types that represent approximately 70% of commercial buildings in the U.S.; for each type, building models are available for each of the three construction categories. The commercial reference buildings (formerly known as commercial building benchmark models) were developed by the U.S. Department of Energy (DOE), in conjunction with three of its national laboratories. Additional data is available directly from DOE's Energy Efficiency & Renewable Energy (EERE) website (see "About Commercial Buildings" resource link), including EnergyPlus software input files (.idf) and results of the EnergyPlus simulations (.html). Note: There have been many changes and improvements since this dataset was released. Several revisions have been made to the models and moved to a different approach to representing typical building energy consumption. For current data on building energy consumption please see the ComStock resource below.

Commercial reference buildings provide complete descriptions for whole building energy analysis using EnergyPlus (see "About EnergyPlus" resource link) simulation software. Included here is data pertaining to the reference building type "Strip Mall" for each of the 16 climate zones described on the Wiki page (see "OpenEI Wiki Page for Commercial Reference Buildings" resource link), and each of three construction categories: new (2004) construction, post-1980 construction existing buildings, and pre-1980 construction existing buildings. The dataset includes four key components: building summary, zone summary, location summary and a picture. Building summary includes details about: form, fabric, and HVAC. Zone summary includes details such as: area, volume, lighting, and occupants for all types of zones in the building. Location summary includes key building information as it pertains to each climate zone, including: fabric and HVAC details, utility costs, energy end use, and peak energy demand. In total, DOE developed 16 reference building types that represent approximately 70% of commercial buildings in the U.S.; for each type, building models are available for each of the three construction categories. The commercial reference buildings (formerly known as commercial building benchmark models) were developed by the U.S. Department of Energy (DOE), in conjunction with three of its national laboratories. Additional data is available directly from DOE's Energy Efficiency & Renewable Energy (EERE) website (see "About Commercial Buildings" resource link), including EnergyPlus software input files (.idf) and results of the EnergyPlus simulations (.html). Note: There have been many changes and improvements since this dataset was released. Several revisions have been made to the models and moved to a different approach to representing typical building energy consumption. For current data on building energy consumption please see the ComStock resource below.